A state estimation method and system for an integrated energy system

Abstract

The present invention provides a state estimation method and system for an integrated energy system, comprising: acquiring measurement data of the integrated energy system; when the acquired measurement data is missing, filling the measurement data based on historical data; inputting the filled measurement data into a state estimation model to obtain the state quantity of the integrated energy system. The present invention detects the completeness of the measurement data before state estimation and fills the missing measurement data when the measurement data is missing, thereby ensuring the integrity and reliability of the measurement data, thereby improving the accuracy of the state estimation of the integrated energy system from the source.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system control, and in particular relates to a state estimation method and system for an integrated energy system. Background Art

[0002] With the continuous development of modern society, people's demand for energy is increasing. Three key concepts have emerged in the energy field: smart grids, integrated energy systems, and the energy internet. These concepts aim to achieve environmentally friendly and sustainable energy supply through both increasing revenue and reducing energy consumption. An integrated energy system, in particular, utilizes advanced physical information technology and innovative management models to integrate multiple energy sources within a region, such as coal, oil, natural gas, electricity, and thermal energy. This system achieves coordinated planning, optimized operation, collaborative management, interactive response, and mutual complementarity among various heterogeneous energy subsystems. While meeting diverse energy demands within the system, it also aims to effectively improve energy utilization efficiency and promote sustainable energy development. To ensure the stability, economy, and safety of the integrated energy system, the automation level of the system dispatch center must be continuously improved. The integrated energy system dispatch center makes decisions based on the system's operating status, as determined by system state estimation. Therefore, the state estimation results are directly related to the safe operation of each energy subsystem in the integrated energy system.

[0003] The various operating data required for the state estimation of the integrated energy system, that is, telemetry information, are collected on-site by the data acquisition and monitoring control system and then transmitted to the integrated energy system dispatching center. However, the system operating data (i.e., raw data) directly provided by the data acquisition and monitoring control system is unstable, which has a huge impact on the subsequent state estimation work. The current treatment of this problem can be summarized into two categories. The first category is to add measurement devices, but considering that the corresponding investment will also increase, the redundancy of the state estimation measurement information has not reached an ideal level; the second category is to innovate in the measurement devices. At present, some measurement devices with higher accuracy have also appeared, and the data transmission performance is relatively stable. However, these measurement devices with excellent performance cannot guarantee large-scale coverage of the integrated energy system. Therefore, how to improve the integrity of the measurement data of the integrated energy system without changing the configuration of the measurement device, and thus improve the accuracy of the state estimation of the integrated energy system, is a problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] To overcome the above-mentioned deficiencies of the prior art, the present invention provides a state estimation method for an integrated energy system, comprising:

[0005] Obtain measurement data of integrated energy systems;

[0006] When the acquired measurement data is missing, filling in the measurement data based on historical data;

[0007] The padded measurement data are input into the state estimation model to obtain the state quantity of the integrated energy system.

[0008] Preferably, the measurement data is supplemented based on historical data, including:

[0009] Obtain corresponding historical measurement data based on missing measurement data;

[0010] Calculating the mathematical expectation value of the historical measurement data;

[0011] The mathematical expectation value of the historical measurement data is used as the filling data to fill the measurement data.

[0012] Preferably, the filled measurement data is input into the state estimation model to obtain the state quantity of the integrated energy system, including:

[0013] Obtain network parameters of integrated energy system power flow model;

[0014] The filled measurement data and the network parameters of the integrated energy system power flow model are input into the state estimation model to obtain the state quantity of the integrated energy system.

[0015] Preferably, the state estimation model is constructed using a weighted least squares algorithm.

[0016] Preferably, the filled measurement data and the network parameters of the integrated energy system power flow model are input into a state estimation model to obtain the state quantity of the integrated energy system, and then the obtained state quantity is revised.

[0017] Preferably, revising the obtained state quantity includes:

[0018] Inputting the padded measurement data into the integrated energy system power flow model to obtain a state quantity output by the integrated energy system power flow model;

[0019] Calculating the deviation between the obtained state quantity and the state quantity output by the integrated energy system power flow model;

[0020] When the deviation is greater than a set deviation threshold, revising the state estimation model based on the padded measurement data and the state quantity output by the integrated energy system power flow model;

[0021] Based on the complete measurement data of the integrated energy system that has been reacquired and the revised state estimation model, the state estimation of the integrated energy system is recalculated until the deviation between the recalculated state estimation of the integrated energy system and the state estimation output by the integrated energy system flow model is less than or equal to the set deviation threshold, and the final state estimation is used as the state estimation result of the integrated energy system.

[0022] Preferably, the measurement data include: grid operation node amplitude, grid operation node injection power, grid line power, gas network pipeline flow, gas network node pressure, gas network node flow, heating network node pressure, heating network supply temperature, heating network return temperature; the state quantity includes: grid node voltage phase angle, grid node voltage amplitude, gas network node pressure, heating network water flow.

[0023] Based on the same concept, the present invention also provides a state estimation system for an integrated energy system, comprising:

[0024] Data acquisition module, used to obtain measurement data of the integrated energy system;

[0025] A data filling module, configured to fill in the missing measurement data based on historical data when the acquired measurement data is missing;

[0026] The state estimation module is used to input the padded measurement data into the state estimation model to obtain the state quantity of the integrated energy system.

[0027] Preferably, the data filling module includes:

[0028] A historical data acquisition unit, configured to acquire corresponding historical measurement data based on the missing measurement data;

[0029] A filling data calculation unit, used to calculate the mathematical expectation value of the historical measurement data;

[0030] The padding data supplementing unit is used to fill the measurement data with the mathematical expectation value of the historical measurement data as the padding data.

[0031] Preferably, the state estimation module includes:

[0032] A network parameter acquisition unit, used to obtain network parameters of the integrated energy system power flow model;

[0033] The state quantity calculation unit is used to input the padded measurement data and the network parameters of the integrated energy system power flow model into the state estimation model to obtain the state quantity of the integrated energy system.

[0034] Compared with the closest prior art, the present invention has the following beneficial effects:

[0035] The present invention provides a state estimation method and system for an integrated energy system, comprising: acquiring measurement data of the integrated energy system; when the acquired measurement data is missing, filling the measurement data based on historical data; inputting the filled measurement data into a state estimation model to obtain the state quantity of the integrated energy system. The present invention detects the completeness of the measurement data before state estimation and fills the missing measurement data when the measurement data is missing, thereby ensuring the integrity and reliability of the measurement data, thereby improving the accuracy of the state estimation of the integrated energy system from the source. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a state estimation method for an integrated energy system provided by the present invention;

[0037] Figure 2 A schematic diagram of a state estimation system for an integrated energy system provided by the present invention. DETAILED DESCRIPTION

[0038] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] Example 1:

[0040] This embodiment provides a state estimation method for an integrated energy system. Figure 1 As shown, including:

[0041] S1 obtains measurement data of the integrated energy system;

[0042] S2: when the acquired measurement data is missing, filling in the measurement data based on historical data;

[0043] S3 inputs the filled measurement data into the state estimation model to obtain the state quantity of the integrated energy system.

[0044] This embodiment takes an integrated energy system including electricity, heat and gas as an example to further illustrate the state estimation method of the integrated energy system. In particular, S1 obtains measurement data of the integrated energy system, including: collecting real-time measurement data X of the system according to the measurement devices installed in the integrated energy system; i0 The measurement data include: grid operation node amplitude, grid operation node injection power, grid line power, gas network pipeline flow, gas network node pressure, gas network node flow, heating network node pressure, heating network supply temperature, and heating network return temperature.

[0045] Furthermore, S2 fills in the missing measurement data based on historical data, including:

[0046] S2-1 collects statistics of the types of measurement data collected in the database and determines the measurement data X i0 Are there any missing quantity measurements?

[0047] S2-2 If the measured data X i0 There is a missing quantity measurement X in i1 , according to the sampling period of the measuring device, the missing quantity measurement X i1 Historical measurement data from the previous week;

[0048] S2-3 In probability theory and statistics, mathematical expectation is the sum of the probability of each possible outcome in an experiment multiplied by its results. It is one of the most basic mathematical characteristics and reflects the size of the average value of a random variable. To ensure the reliability of the filled data, this embodiment uses the mathematical expectation filling method to fill in the missing measurement data, that is, the mathematical expectation value E of the historical measurement data is calculated according to the mathematical expectation filling formula. k+1 , and the mathematical expectation value E k+1 Measure X as the missing quantity i1 Fill in the data to add to the measurement data X i0 In the mathematical expectation filling method, the filling formula is as follows:

[0049]

[0050] Where: k is the number of historical measurement data selected in the previous week; X i is the value of the i-th historical measurement data; P i is the probability that the value of the i-th historical measurement data appears in the historical measurement data (that is, the number of times this data appears in the k historical measurement data).

[0051] In this embodiment, the mathematical expectation value of the historical measurement data is used to fill in the missing measurement data. Other data filling methods may also be used to fill in the missing measurement data.

[0052] Furthermore, S3 inputs the filled measurement data into the state estimation model to obtain the state quantities of the integrated energy system, including:

[0053] S3-1 obtains network parameters of the integrated energy system power flow model;

[0054] S3-2 measures the data X i0 The network parameters of the integrated energy system power flow model are input into the state estimation model to calculate the state estimation X of the integrated energy system. i4 ,The state quantities of the integrated energy system include: grid node voltage phase angle, grid node voltage amplitude, gas network node pressure, and heat network water flow;

[0055] In this embodiment, the state estimation model is constructed using the weighted least squares algorithm, and the objective function is as follows:

[0056] J(x)=[zh(x)] T R -1 [zh(x)]

[0057] Where z is the measurement vector; x is the state estimate; h(x) is the measurement function. Measurement functions for different energy measurement methods are available in the literature; and R is a weighted diagonal matrix. Given the measurement, network parameters, and structure, the state estimate x is the state value that minimizes the objective function J(x). Since h(x) is a measurement function of x, the state estimate x cannot be directly calculated. Therefore, the Newton iteration method is used to solve the state estimate x.

[0058] S3-3 To improve the accuracy of the state estimation of the integrated energy system, the accuracy of the state estimation is further evaluated after step S3-2, and the state estimation results are revised, including:

[0059] The real-time collected quantity is measured X i0 Input the network structure of the integrated energy system to build a coupled power flow model of the integrated energy system and obtain a set of running state data X i3 , the state data X i3 As the true value of the state estimation result evaluation;

[0060] Calculate the state estimate X i4 The true value X evaluated with the state estimation result i3 The root mean square error between i4 With reference value X i3 The deviation between the two is used to set a deviation threshold. If the deviation from the threshold is large, the state estimation model is modified and the measurement data is recollected for state estimation. If the deviation from the threshold is small, the state estimation value is output. The root mean square error is expressed as follows:

[0061]

[0062] Where: N represents the dimension of the state quantity; Represents the i-th component of the estimated value of the state quantity; The i-th component of the truth value of the state quantity.

[0063] Furthermore, considering the three energy sources of electricity, heat, and gas, the coupling element uses gas turbine equipment to construct a coupled power flow model of the integrated energy system. The power flow equations of each energy system are as follows:

[0064] Power system: Assume that the power system has n nodes and m PQ nodes. Write the active and reactive power equations for the power system separately. The power flow equation in polar coordinate form is as follows:

[0065]

[0066]

[0067] The above equations can be expressed as the following nonlinear vector equation:

[0068]

[0069] Where ΔP(θ, V) and ΔQ(θ, U) represent the n-1-dimensional active power vector function and the m-dimensional reactive power vector function, respectively; θ represents the voltage phase angle vector of the PQ and PV nodes; U represents the voltage vector of the PQ node; and V represents the voltage vector of the PQ and PV nodes.

[0070] The pipeline flow-pressure balance formula for natural gas is as follows:

[0071]

[0072]

[0073] Where: f kij is the pipeline flow rate; F k is the pipeline friction coefficient; D k is the inner diameter of the pipe between nodes; G is the specific gravity of gas; L k is the length of the pipeline between nodes;

[0074] The network heat loss of heat medium transmission in the heat network system can be expressed as:

[0075]

[0076] Where: is the total heat loss in the thermal system; T0 is the initial temperature; T e is the average temperature of the medium surrounding the pipeline; T rw is the return water temperature of the heating system; H0 is the available thermal power of the heat medium; ∑R is the total thermal resistance per kilometer of pipeline between the heat medium and the surrounding medium; l is the length of the pipe section.

[0077] Example 2:

[0078] This embodiment discloses a state estimation system for an integrated energy system. Figure 2 As shown, including:

[0079] Data acquisition module, used to obtain measurement data of the integrated energy system;

[0080] A data filling module, configured to fill in the missing measurement data based on historical data when the acquired measurement data is missing;

[0081] The state estimation module is used to input the padded measurement data into the state estimation model to obtain the state quantity of the integrated energy system.

[0082] Data filling module, including:

[0083] A historical data acquisition unit, configured to acquire corresponding historical measurement data based on the missing measurement data;

[0084] A filling data calculation unit, used to calculate the mathematical expectation value of the historical measurement data;

[0085] The padding data supplementing unit is used to fill the measurement data with the mathematical expectation value of the historical measurement data as the padding data.

[0086] State estimation module, including:

[0087] A network parameter acquisition unit, used to obtain network parameters of the integrated energy system power flow model;

[0088] The state quantity calculation unit is used to input the padded measurement data and the network parameters of the integrated energy system power flow model into the state estimation model to obtain the state quantity of the integrated energy system.

[0089] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit its scope of protection. Although the present application has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading this application, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the application.

Claims

1. A state estimation method for an integrated energy system, characterized in that: include: Obtain measurement data of integrated energy systems; When the acquired measurement data is missing, filling in the measurement data based on historical data; Input the filled measurement data into the state estimation model to obtain the state quantity of the integrated energy system; The padded measurement data is input into the state estimation model to obtain the state quantity of the integrated energy system, including: Obtain network parameters of integrated energy system power flow model; Inputting the filled measurement data and the network parameters of the integrated energy system power flow model into a state estimation model to obtain the state quantity of the integrated energy system; The step of inputting the filled measurement data and the network parameters of the integrated energy system power flow model into the state estimation model to obtain the state quantity of the integrated energy system also includes revising the obtained state quantity; The revising of the obtained state quantity includes: Inputting the padded measurement data into the integrated energy system power flow model to obtain a state quantity output by the integrated energy system power flow model; Calculating the deviation between the obtained state quantity and the state quantity output by the integrated energy system power flow model; When the deviation is greater than a set deviation threshold, revising the state estimation model based on the padded measurement data and the state quantity output by the integrated energy system power flow model; Based on the complete measurement data of the integrated energy system that has been reacquired and the revised state estimation model, the state estimation of the integrated energy system is recalculated until the deviation between the recalculated state estimation of the integrated energy system and the state estimation output by the integrated energy system flow model is less than or equal to the set deviation threshold, and the final state estimation is used as the state estimation result of the integrated energy system.

2. The method according to claim 1, wherein The filling of the measurement data based on historical data includes: Obtain corresponding historical measurement data based on missing measurement data; Calculating the mathematical expectation value of the historical measurement data; The mathematical expectation value of the historical measurement data is used as the filling data to fill the measurement data.

3. The method according to claim 1, wherein The state estimation model is constructed using a weighted least squares algorithm.

4. The method according to claim 1, wherein The measurement data include: grid operation node amplitude, grid operation node injection power, grid line power, gas network pipeline flow, gas network node pressure, gas network node flow, heating network node pressure, heating network supply temperature, heating network return temperature; the state quantity includes: grid node voltage phase angle, grid node voltage amplitude, gas network node pressure, heating network water flow.

5. A state estimation system for an integrated energy system, characterized in that: include: Data acquisition module, used to obtain measurement data of the integrated energy system; A data filling module, configured to fill in the missing measurement data based on historical data when the acquired measurement data is missing; The state estimation module is used to input the padded measurement data into the state estimation model to obtain the state quantity of the integrated energy system; The state estimation module comprises: A network parameter acquisition unit, used to obtain network parameters of the integrated energy system power flow model; A state quantity calculation unit is used to input the padded measurement data and the network parameters of the integrated energy system power flow model into a state estimation model to obtain the state quantity of the integrated energy system; The state quantity calculation unit further includes revising the obtained state quantity; The revising of the obtained state quantity includes: Inputting the padded measurement data into the integrated energy system power flow model to obtain a state quantity output by the integrated energy system power flow model; Calculating the deviation between the obtained state quantity and the state quantity output by the integrated energy system power flow model; When the deviation is greater than a set deviation threshold, revising the state estimation model based on the padded measurement data and the state quantity output by the integrated energy system power flow model; Based on the complete measurement data of the integrated energy system that has been reacquired and the revised state estimation model, the state estimation of the integrated energy system is recalculated until the deviation between the recalculated state estimation of the integrated energy system and the state estimation output by the integrated energy system flow model is less than or equal to the set deviation threshold, and the final state estimation is used as the state estimation result of the integrated energy system.

6. The system according to claim 5, wherein: The data filling module includes: A historical data acquisition unit, configured to acquire corresponding historical measurement data based on the missing measurement data; A filling data calculation unit, used to calculate the mathematical expectation value of the historical measurement data; The padding data supplementing unit is used to fill the measurement data with the mathematical expectation value of the historical measurement data as the padding data.

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