Cooperative control and scheduling system for multi-energy complementary energy router

Abstract

The invention relates to the technical field of electric power system control and multi-energy complementation, and discloses a multi-energy complementation type energy router cooperative control and scheduling system, and the system employs a state construction module to accurately map the effective power boundary and response hysteresis characteristics of a thermodynamic system into a DC side virtual reference capacitor and a virtual resistor. Establishing a complex field impedance model; the short-circuit ratio of an external power grid is estimated in real time through an intensity identification module, and the access proportion of virtual impedance is adaptively adjusted by an impedance synthesis module according to the intensity of the power grid; and the control execution module generates a driving signal of the voltage source type converter under the synchronous rotating coordinate system based on the impedance correction matrix containing the cross coupling term. The problem that a traditional energy router lacks physical inertia is solved, inertial support of a thermodynamic system to a power grid is achieved through thermal-electric physical mapping and power grid strength self-adaptive control, and broadband oscillation under the weak network condition is effectively restrained.

Description

Technical Field

[0001] This invention relates to the field of power system control and multi-energy complementarity technology, specifically to a multi-energy complementary energy router collaborative control and scheduling system. Background Technology

[0002] With the development of energy internet technology, energy routers that integrate multiple energy forms such as electricity, heat, and cooling are gradually becoming the core hub of integrated energy systems. Energy routers mainly rely on voltage source converters to realize AC-DC conversion and power transmission of electrical energy. This power electronic interface characteristic makes its operating mechanism significantly different from that of traditional synchronous generators.

[0003] In existing technologies, voltage source converters typically employ phase-locked loops (PLLs) to follow the grid voltage phase, exhibiting zero or low inertia control characteristics. When external grid frequencies drift or voltage dips occur, the converter lacks the kinetic energy buffering effect of a physical rotor, making it difficult to provide instantaneous power support and leading to a decrease in system frequency and voltage stability. Although virtual synchronous machine (VSM) or virtual capacitor (VFC) technologies are commonly used to simulate inertial characteristics, these technologies often rely excessively on physical capacitors or additional electrochemical energy storage batteries configured on the DC bus side. Physical capacitors have low energy density, making it difficult to maintain power throughput over long periods; while electrochemical energy storage batteries, although having large capacity, suffer from short cycle life, slow charge / discharge response, and high construction costs, limiting the large-scale application of virtual inertial control.

[0004] Meanwhile, the local thermal systems connected to energy routers typically possess enormous heat capacity, containing far more energy than the energy storage units on the electrical side. However, existing control strategies often decouple the thermal and electrical systems as two independent time scales, focusing only on steady-state energy balance and neglecting the potential support value of the thermal system's energy throughput capacity over long time constants for transient processes on the electrical side. This separation of physical characteristics across energy domains results in a significant amount of idle physical inertial resources within the system, failing to be transformed into the damping support urgently needed by the electrical side.

[0005] Furthermore, the operating environment of energy routers is becoming increasingly complex, with the grid strength at the point of common junction exhibiting significant time-varying characteristics. In scenarios involving long-distance power transmission or weakly connected grids with a high proportion of renewable energy, the grid exhibits high impedance characteristics. If the control system uses fixed impedance parameters, it is highly susceptible to coupling resonance with the grid impedance under weak grid conditions, leading to broadband oscillations or even system instability. Existing parameter self-tuning methods mostly rely on offline design, lacking online identification and proactive adaptation mechanisms for the real-time short-circuit ratio of the grid, making it difficult to balance the speed under strong grid conditions with the stability under weak grid conditions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a multi-energy complementary energy router collaborative control and scheduling system, which solves the problems of existing energy routers lacking physical inertia and damping, being prone to broadband oscillations under weak grid connection conditions, and failing to effectively utilize the energy storage characteristics of local thermal systems for cross-energy domain electrical support.

[0007] To achieve the above objectives, the first aspect of the present invention provides a multi-energy complementary energy router collaborative control and scheduling system. The energy router is connected to the external power grid through a common connection point and coupled to the local thermal system through a thermal interface. The energy router is equipped with a central controller, which includes a data acquisition module, a state construction module, an intensity identification module, an impedance synthesis module, and a control execution module.

[0008] The data acquisition module is used to acquire electrical signals from the common connection point and thermal status signals from the local heating system in real time, providing a data foundation for subsequent control.

[0009] The state construction module is mainly used to solve the equivalent mapping problem of heterogeneous energy flow. Based on the thermodynamic law of conservation of energy and combined with the operating temperature boundary of the thermodynamic system, this module calculates the effective power boundary that the thermodynamic system can provide within the transient control time window of the power grid.

[0010] The state construction module maps the effective power boundary to a virtual reference capacitor on the DC side, so that the energy storage capacity of the thermal system is represented as capacitive characteristics in the electrical model. Meanwhile, the module maps the step response time constant of the thermal system to a virtual resistance connected in series with the virtual reference capacitor. Through the above mapping, a complex domain impedance model containing damping characteristics is established, thereby using the physical inertia of the thermal system to suppress power fluctuations on the electrical side.

[0011] The intensity identification module is used to sense the strength characteristics of the external power grid. This module quantifies the electrical excitation level by calculating the norm of the rate of change of voltage and current at the point of common coupling. When the electrical excitation level is lower than the preset excitation threshold, the module locks the estimated value of the power grid equivalent impedance at the previous moment to prevent the algorithm from diverging in steady state. When the electrical excitation level is higher than or equal to a preset excitation threshold, the module initiates a recursive least squares algorithm to update the Thevenin equivalent parameters of the external power grid, and then calculates the real-time short-circuit ratio. This mechanism ensures that the system can accurately identify the time-varying characteristics of the power grid strength.

[0012] The impedance synthesis module is used to dynamically adjust the control strategy based on the grid strength. This module establishes a nonlinear mapping relationship between the real-time short-circuit ratio and the adaptive gain, and uses the adaptive gain to adjust the connection ratio of the virtual reference capacitor.

[0013] Under strong network conditions, increase the access ratio to fully utilize thermal support capacity; Under weak network conditions, the access ratio is reduced to maintain phase stability. In addition, the module also combines virtual resistors to synthesize the total target input impedance for a specific oscillation frequency.

[0014] The control execution module is used to convert complex domain impedance into time domain control commands. In a synchronous rotating coordinate system, the module calculates the virtual capacitive reactance modulus at the fundamental frequency and constructs an impedance correction matrix. The main diagonal elements of the matrix are virtual resistances, and the secondary diagonal elements are virtual capacitive reactance modulus values. By using this impedance correction matrix to perform calculations on the current components, a voltage correction vector containing cross-coupling terms is generated.

[0015] The direct-axis voltage correction includes the positive coupling component generated by the quadrature-axis current, and the quadrature-axis voltage correction includes the negative coupling component generated by the direct-axis current. This cross-coupling operation physically replicates the voltage hysteresis characteristics of capacitive impedance. Finally, the control execution module superimposes the voltage correction onto the output of the current inner loop controller to generate a pulse signal to drive the voltage source converter.

[0016] A second aspect of this invention provides a method for coordinated control and scheduling of multi-energy complementary energy routers, which achieves operation control of the energy routers through the following steps: First, the system collects electrical data from common connection points and medium temperature and fluid pressure data from the thermal system in real time.

[0017] Secondly, based on the collected thermal data, the effective power boundary within the power grid transient control time window is calculated. Utilizing the principle of energy conservation, the effective power boundary is transformed into a virtual reference capacitor on the DC side, and the response hysteresis characteristics of the thermal system are transformed into a virtual resistance connected in series with the virtual reference capacitor, thereby generating a complex domain impedance model.

[0018] Subsequently, the system identifies the strength of the external power grid. It calculates the rate of change norm of the voltage and current at the point of common coupling. If the rate of change norm indicates that the electrical excitation level is sufficient, it updates the Thevenin equivalent parameters of the external power grid using a recursive algorithm and calculates the real-time short-circuit ratio; otherwise, it retains the identification results from the previous time step.

[0019] Furthermore, the adaptive gain is calculated based on the real-time short-circuit ratio, the connection ratio of the virtual reference capacitor is dynamically adjusted, and the total target input impedance is synthesized by combining it with the virtual resistance. This step achieves adaptive matching of the control strategy to the grid strength.

[0020] Finally, impedance control is performed in a synchronously rotating coordinate system. The virtual capacitive reactance modulus is calculated and an impedance correction matrix is ​​constructed. The voltage correction amount generated by the total target input impedance is calculated through matrix operations.

[0021] The voltage correction is superimposed on the control loop as a feedforward or feedback signal to generate a space vector pulse width modulation signal, which drives the power devices inside the energy router to achieve inertial support and oscillation suppression of the power system by the thermal system.

[0022] This invention provides a multi-energy complementary energy router collaborative control and scheduling system. It has the following beneficial effects: 1. This invention maps the effective power boundary and response hysteresis characteristics of a thermal system to virtual reference capacitance and virtual resistance on the DC side through a state construction module. This mapping mechanism directly utilizes the thermal inertia of the thermal system to provide equivalent physical inertia and damping for power electronic devices, solving the problem of poor anti-disturbance capability caused by the lack of mechanical rotating parts in traditional energy routers, and realizing millisecond-level power support from the thermal energy domain to the electrical energy domain.

[0023] 2. This invention adopts a grid strength identification mechanism based on electrical excitation level judgment and an adaptive impedance synthesis strategy. The system updates grid parameters only when the voltage and current change rate at the point of common coupling meets the threshold, avoiding the divergence of the recursive algorithm under steady state. At the same time, the connection ratio of virtual capacitors is automatically adjusted according to the real-time short-circuit ratio. Under strong grid conditions, the thermal support capability is maximized, and under weak grid conditions, the control gain is reduced to prevent phase instability, thereby ensuring the operational stability of the system under different grid strengths.

[0024] 3. In the control execution stage, this invention constructs an impedance correction matrix containing cross-coupling terms, and performs off-diagonal correction on the direct axis and quadrature axis voltages in a synchronously rotating coordinate system. This operation logic mathematically and rigorously reproduces the physical characteristic that capacitive impedance voltage lags behind current, corrects the phase deviation caused by neglecting the coordinate system rotation effect when conventional decoupling control simulates a large-capacity virtual capacitor, and ensures that the virtual impedance control command can be accurately executed by the converter. Attached Figure Description

[0025] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention.

[0026] Among them, 101 is the data acquisition module; 102 is the state construction module; 103 is the intensity identification module; 104 is the impedance synthesis module; and 105 is the control execution module. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example: Please see the appendix Figure 1 This invention provides a multi-energy complementary energy router collaborative control and scheduling system. The energy router is connected to the external power grid through a common connection point, realizes power conversion using an internal voltage source converter and DC bus, and couples to the local thermal system through a thermal interface. The energy router is equipped with a central controller, which runs multi-energy impedance collaborative control logic.

[0029] The central controller includes the following logical function modules: The system includes a data acquisition module 101, a state construction module 102, an intensity identification module 103, an impedance synthesis module 104, and a control execution module 105.

[0030] The data acquisition module 101 is responsible for acquiring the basic physical quantities of the system operation, which cover the electrical and thermal domains.

[0031] The state construction module 102 establishes a power support model that includes physical time constant constraints, which is used to calculate the effective support capability of non-electrical systems for electrical systems.

[0032] The strength identification module 103 is used to estimate the short-circuit capacity and short-circuit ratio of the external power grid at the point of common coupling in real time, and has a locking mechanism to prevent algorithm divergence.

[0033] Impedance synthesis module 104 integrates physical capabilities and environmental constraints to generate target control commands.

[0034] The control execution module 105 converts the target control command into a pulse width modulation signal that drives the voltage source converter.

[0035] Please see the appendix Figure 2 This invention provides a method for coordinated control and scheduling of multi-energy complementary energy routers, comprising the following steps: S10, the data acquisition module 101 acquires the three-phase voltage, three-phase current, DC bus voltage, and medium temperature and fluid pressure of the common connection point in real time, and converts the analog signals into discrete digital signals.

[0036] S20, the state construction module 102 calculates the effective power boundary within the power grid transient control time window based on the medium temperature, fluid pressure and preset step response time constant of the thermal system. The state construction module 102 maps the effective power boundary to a DC-side virtual reference capacitor and maps the response hysteresis characteristics of the thermodynamic system to a virtual resistor connected in series with the virtual reference capacitor, generating a complex domain impedance model that includes damping characteristics.

[0037] S30, the intensity identification module 103 calculates the rate of change norm of the voltage and current at the point of common coupling as the electrical excitation level, and compares the electrical excitation level with the preset excitation threshold. When the electrical excitation level is lower than the excitation threshold, the intensity identification module 103 locks the previous moment's estimated power grid equivalent impedance value; When the electrical excitation level is higher than or equal to the excitation threshold, the intensity identification module 103 uses the recursive least squares algorithm to update the Thevenin equivalent parameters of the external power grid and calculate the real-time short-circuit ratio.

[0038] S40, the impedance synthesis module 104 extracts the low-frequency oscillation component of the common coupling point voltage and calculates the adaptive gain of the power grid strength based on the real-time short-circuit ratio; The impedance synthesis module 104 uses adaptive gain to adjust the connection ratio of the virtual reference capacitor and combines it with virtual resistance to synthesize the total target input impedance for a specific oscillation frequency.

[0039] S50, the control execution module 105 calculates the voltage correction amount generated by the total target input impedance in the synchronous rotating coordinate system; The control execution module 105 superimposes the voltage correction amount onto the output of the current inner loop controller to generate a reference voltage vector, and generates a drive signal through space vector pulse width modulation to control the operation of the power devices of the energy router.

[0040] The data acquisition module 101 acquires physical signals reflecting the real-time operating status of the system through a sensor network distributed at key nodes of the energy router. In order to achieve unified control of multiple physical fields of electricity, heat and gas and avoid information silos between different energy flow subsystems, the data acquisition module 101 establishes a global state vector containing electrical and non-electrical quantities, and uses this vector as the sole data source for subsequent state space construction and impedance synthesis.

[0041] S101, the data acquisition module 101 defines and acquires a seven-dimensional augmented state vector at each sampling time tt. The physical basis for choosing this seven-dimensional vector is as follows: Electrical quantities directly reflect the current power demand and port voltage level of the power grid, while non-electrical quantities characterize the maximum power buffer depth that the local energy system can provide through the multi-energy conversion interface. Together, they constitute the boundary conditions for source-grid-load coordinated control.

[0042] State vector The specific structure is shown in the following formula: ; In the formula, and These represent the direct-axis and quadrature-axis components of the voltage at the point of common coupling in a synchronous rotating coordinate system, respectively, in volts (V). and These represent the direct-axis and quadrature-axis components of the current at the point of common coupling in the synchronous rotating coordinate system, respectively, in amperes (A). It represents the real-time voltage value of the DC bus and is used to monitor the energy balance status on the DC side. This indicates the current average temperature of a portion of the medium within the thermal buffer system, expressed in degrees Celsius (°C). This indicates the current pressure value of the fluid subsystem, in Pascals (Pa).

[0043] For temperature and pressure For these non-electrical quantities, the data acquisition module 101 converts the analog signal into a standard electrical signal through a high-precision temperature change sensor, and then performs discrete sampling through an analog-to-digital converter.

[0044] S102. Regarding the coordinate transformation of electrical quantities, in order to convert the time-varying sinusoidal AC quantity into a DC steady-state quantity suitable for linear PI control, the data acquisition module 101 uses a phase-locked loop to track the phase angle of the three-phase voltage at the point of common coupling in real time. .

[0045] Based on this phase angle The data acquisition module 101 acquires the voltage signals in the three-phase stationary coordinate system. and current signal Perform the Park transformation and project it onto the synchronously rotated dq coordinate system. The specific transformation operation is as follows: ; In the formula, Represents voltage vector or current vector ,coefficient Used to ensure the amplitude remains unchanged before and after the transformation.

[0046] Through this transformation, the AC component of the power frequency is converted into a DC component, achieving decoupled control of active and reactive currents. It should be noted that during the above matrix operation, if the phase-locked loop loses its lock, resulting in… In the event of a sudden change, from the perspective of algorithm robustness, the module will automatically switch to the phase angle estimate of the previous moment for inertial maintenance until the phase lock stabilizes again, in order to prevent oscillation of the dq axis components.

[0047] The specific hardware circuit implementation or digital algorithm flow of the Park transform is a well-known technology in the field of power electronic control, and will not be elaborated here.

[0048] The core function of the state construction module 102 is to solve the mismatch problem between the large inertial time scale of the non-electrical thermodynamic system and the small inertial time scale of the electrical system. In response to the shortcomings of existing technologies that directly simplify multi-energy coupled systems as constant power sources and ignore physical response lag, this module uses a physical mapping algorithm to transform the energy buffering capacity of the thermodynamic system into equivalent complex impedance parameters in the electrical control domain. This allows the subsequent controller to access the energy storage capacity of the thermodynamic system like a capacitor, while automatically avoiding the physical lag limitation of the thermodynamic response by utilizing virtual resistance characteristics.

[0049] S201, State Construction Module 102, based on the thermodynamic law of conservation of energy, pre-calculates the current steady-state energy reserve capacity of the thermal buffer system before any control action is executed.

[0050] The module reads the current medium temperature uploaded by the data acquisition module. and fluid pressure The module is based on real-time fluid pressure. Query the preset thermodynamic property database to obtain the isobaric specific heat capacity of the medium at the current pressure. And the corresponding saturation temperature boundary, thereby dynamically correcting the maximum safe operating temperature allowed by the system. With minimum safe operating temperature .

[0051] Subsequently, to ensure the absolute safety of the system during the bidirectional regulation process of energy charging (heat absorption) and energy release (heat release), the module calculates the minimum thermal margin between the current temperature point and the safety boundary. The calculation process is shown in the following formula: ; In the formula, The mass of the fluid involved in heat exchange is expressed in kilograms. The function is for finding the minimum value; It is the absolute value symbol.

[0052] This step quantifies the maximum static energy limit that the thermal system can theoretically utilize as a bidirectional energy buffer without triggering a safety protection shutdown by selecting the minimum distance from the current temperature to the upper and lower temperature limits.

[0053] S202. Considering that the energy release rate is limited by the physical conduction process, the state construction module 102 introduces a time constant constraint to calculate the effective power boundary within the power grid transient control time window. .

[0054] Because the energy release of a thermal system is limited by the heat transfer efficiency of the heat exchanger and the fluid transport velocity, it cannot release heat instantaneously. Therefore, the module introduces the step response time constant of the thermodynamic system. (The time required for the thermal system output to reach 63.2% of its steady-state value), and the time window required for primary frequency regulation or oscillation suppression of the power grid. .

[0055] In this embodiment, Configured to be 0.1 to 0.5 times the period of the grid-dominant low-frequency oscillation (0.1 Hz to 2.5 Hz) to ensure that thermal support can cover the critical damping region of electrical oscillations. Effective power boundary. Indicates in Within a given time frame, considering the physical lag characteristics, the maximum heat power that can actually be converted into electrical power is calculated using the following criteria: ; In the formula, The thermal-electric energy utilization efficiency coefficient, with a range of values. It is used to characterize the conversion losses of a heat engine or thermoelectric generator; The base is the natural logarithm. This is used to enhance the numerical robustness of the algorithm and prevent errors due to incorrect parameter configuration during computation. A value of 0 causes division overflow; the formula uses... Logic, in which The minimum physical time constant limit allowed by the system (e.g., 1 ms).

[0056] The physical meaning of this formula is: by using an exponential decay function to eliminate invalid energy that cannot respond in time due to thermal inertia, only the power component that can be effective during the sensitive period of electrical control is retained.

[0057] S203, State construction module 102 performs energy-capacitance mapping and calculates the virtual reference capacitance value on the DC side. In order for a DC bus voltage source converter (VSC) to identify the support capacity of the thermal system, thermal power must be mapped to electrical parameters.

[0058] Based on the principle of energy conservation, the module assumes that within the power grid transient control time window... Within this, the energy released by the virtual capacitor should be equal to the effective power boundary. The total energy output during this period (i.e. .

[0059] Based on capacitor energy storage formula The inverse operation yields the calculation model for the virtual reference capacitor: ; In the formula, This is the rated voltage of the DC bus, in volts (V). This is the minimum permissible operating voltage for the DC bus. The time window for transient control of the power grid as defined in the preceding steps (e.g., 0.1s to 0.5s); To prevent tiny voltage thresholds where the denominator is zero.

[0060] By correcting the time variable from a microsecond-level sampling period to a second-level control window time, the formula calculates... This can truly reflect the potential of the thermal system as a large-capacity energy reservoir to support the electrical side, making the energy router appear as an electrical node with a huge equivalent capacitance in the mathematical model.

[0061] S204, State Construction Module 102 constructs a physical protection mechanism, mapping the response hysteresis characteristics of the thermal system to a virtual resistance. This is a crucial step in preventing the control system from drawing instantaneous power from a sluggish thermal system, which could lead to DC voltage collapse.

[0062] The virtual resistor not only dampens oscillations but also provides physical protection by limiting current. The value was designed to be consistent with the thermodynamic time constant. Positively correlated with virtual capacitance They are inversely correlated, forming a high-pass filter impedance characteristic with a specific cutoff frequency. Its mapping logic is as follows: ; In the formula, This is a dimensionless damping adjustment coefficient, typically ranging from 0.5 to 5.0, used to fine-tune the damping strength according to system stability requirements; To prevent regularization parameters from having a denominator of zero, the value is set to [value to be filled in]. This formula clarifies the physical coupling relationship between the two: The slower the response of a thermal system ( The larger the value, the greater its series internal resistance in the electrical model. The larger the current, the more it limits the rate of change of current, forcing a smooth transition of power requests on the electrical side, thus embedding physical constraints into the control model.

[0063] Finally, the output of state construction module 102 is... and The complex domain impedance model, formed by series connection, is then used in the subsequent impedance synthesis module.

[0064] The strength identification module 103 aims to solve the parameter divergence problem caused by insufficient excitation in traditional impedance identification algorithms when the power grid is operating in steady state. This module uses the recursive least squares (RLS) method with dead zone threshold to update the grid-side impedance estimate only when the system detects sufficient voltage and current disturbances, thereby robustly outputting the short-circuit ratio (SCR) index, which characterizes the strength of the power grid.

[0065] S301, the strength identification module 103 constructs a Thevenin equivalent circuit model based on the point of common coupling (PCC). In order to adapt to the control strategy under the synchronous rotating coordinate system, the module equates the three-phase AC power grid to a series resistor. and inductor The voltage source.

[0066] Considering the grid electromotive force The amplitude changes slowly over a short period of time, while the PCC point voltage... With current The load fluctuates rapidly, so the module constructs a linear regression equation based on incremental form: ; In the formula, This represents the difference between the current value and the previous value. The fundamental angular frequency of the power grid; This is a discrete-time index.

[0067] To transform the above differential equations into an algebraic form suitable for discrete iterative operations, the module uses the backward Euler method to discretize them and then rearranges them into a standard least squares scheme. .

[0068] The parameter vector to be identified is defined as follows: This step establishes a linear mapping relationship between the electrical quantity fluctuations at the PCC point and the physical impedance parameters on the grid side.

[0069] S302. To assess the information richness of the current data sample, the intensity identification module 103 defines and calculates the electrical excitation function. During steady-state operation, the voltage and current are relatively stable, and the signal-to-noise ratio of the input data is extremely low. If parameter fitting is forced, the calculated impedance value will be dominated by random noise and will drift drastically.

[0070] The module quantifies the current disturbance strength by calculating the Euclidean norm of the dq-axis voltage and current increments: ; In the formula, This is the current weighting factor, used to balance the order-of-magnitude difference between voltage and current. It is typically taken as the square of the system's rated impedance. After calculation, the module will... With the preset excitation threshold Compare them. The value is determined based on 3 to 5 times the system sensor noise floor level, ensuring that the algorithm can only be activated when the physical disturbance is significantly greater than the measurement noise.

[0071] S303, the intensity identification module 103 executes a thresholded recursive least squares (RLS) algorithm iteration. This step includes two parallel logical branches: silent period locking and activation period updating. The specific execution logic is as follows: when At this time, the system determines that it is in a silent period. During this period, the module forcibly locks the estimated parameters and covariance matrix, that is, it directly sets... and This prevents overflow in subsequent calculations caused by the diagonal elements of the covariance matrix growing infinitely over time in an unexcited state.

[0072] when At this point, the system determines that it is in the excitation period. The module updates the gain vector using the new data. Parameter estimates and covariance matrix The specific iterative formula is as follows: ; ; ; In the formula, This is the forgetting factor, and its value range is usually [value range missing]. This is to enable the algorithm to better reflect recent changes in power grid characteristics; The identity matrix. Initial covariance matrix. Set as a large diagonal matrix (e.g.) This is used to characterize the extreme uncertainty of the initial estimate, thereby accelerating the initial convergence process.

[0073] S304, Based on the identified resistor With inductance The convergence value and strength identification module 103 calculate the comprehensive impedance modulus and short-circuit ratio of the power grid. To avoid misjudgments caused by fluctuations in a single parameter, the module outputs the impedance modulus after moving average filtering.

[0074] Final short-circuit ratio The calculation formula is as follows: ; ; In the formula, This is the rated effective value of the line voltage at the PCC point; This refers to the rated apparent power of the energy router.

[0075] In this calculation step, add an exception protection logic: if the calculated... If the value is less than the physical minimum limit (e.g., the impedance of the line itself), then this minimum value is used to replace the denominator in the calculation to prevent the SCR value from approaching infinity. The calculated value... It will be used as a key state variable to characterize the strength of the power grid and will be transmitted in real time to the subsequent impedance synthesis module.

[0076] Impedance synthesis module 104 aims to establish a dynamic balance between thermal-side physical constraints and grid-side electrical demands. By introducing a real-time grid strength feedback mechanism, it solves the problem of poor adaptability of constant parameter control in variable grid environments.

[0077] This module uses a nonlinear gain function based on the short-circuit ratio to dynamically reconstruct the target input impedance of the converter. This allows the system to maximize its auxiliary service capabilities without violating physical limitations.

[0078] S401. In view of the limitations of traditional fast Fourier transform, such as fixed window length leading to spectral leakage and large computational load, which makes it difficult to meet the real-time control requirements, the impedance synthesis module 104 adopts an algorithm based on sliding discrete Fourier transform (SDFT) to extract the low-frequency oscillation characteristics of the PCC point.

[0079] The SDFT utilizes the recursive properties of a frequency-domain comb filter to update the amplitude and phase of specific frequency components point-by-point in each sampling period. Its computational complexity is independent of the sliding window length and is linearly related only to the number of frequency points of interest. The recursive operator of the SDFT is shown below: ; In the formula, The current sampling time The corresponding number Complex spectral coefficients of the second harmonic components; This is the latest input sampling point; These are the old sampling points that were moved out by the sliding window; The sliding window length is determined by the sampling frequency. and fundamental frequency Decision, that is ; To prevent the forgetting factor from accumulating numerical integration errors, the value range is [0.999, 1.0].

[0080] The physical meaning of this formula lies in achieving real-time spectrum updates by shifting in new data and out old data. To ensure the reliability of oscillation detection and avoid false triggering by transient impacts, the module monitors... In the amplitude spectrum of the low-frequency band (0.1Hz to 2.5Hz), the amplitude of a certain frequency component is continuous if and only if the amplitude of the component is continuous. The time (e.g., 3 fundamental frequency cycles) exceeds the preset oscillation threshold. Only then is the dominant oscillation frequency locked. .

[0081] S402, Impedance Synthesis Module 104 calculates the Sigmoid adaptive gain function based on the short-circuit ratio (SCR). This step is based on the control principle of strong grids receiving multiple assistance and weak grids protecting themselves, and establishes a nonlinear mapping between grid strength and virtual inertial support strength.

[0082] When the power grid is robust (with a large SCR), the system should make full use of the buffer capacity of the thermal system to provide inertial support; When the power grid is extremely weak (SCR is small), excessive external eigenvalue capacitiveness can lead to insufficient system phase margin and trigger subsynchronous oscillations. In this case, rapid attenuation of the support force is necessary. The module uses the Sigmoid function because it has the characteristics of saturation at both ends and linearity in the middle, which can smoothly handle the transition region between strong and weak grids. The calculation model is as follows: ; In the formula, The maximum allowable gain is set to 1.0, representing 100% utilization of the thermal buffering capacity. To minimize the retained gain, a value of 0.1 is set to retain the minimum voltage regulation capability to maintain system operation; This is the sensitivity factor, with a value range of [3.0, 5.0]. The larger the value, the faster the switching near the critical point. The threshold for the center short-circuit ratio during mode switching is set according to the weak network boundary line in the power system industry standard, and is usually taken as 2.5. The real-time short-circuit ratio is input to the strength identification module 103. Before calculation. Apply amplitude limiting to... The range is used to prevent the gain calculation from diverging due to extreme SCR values ​​caused by measurement noise.

[0083] S403 and impedance synthesis module 104 integrate thermodynamic constraint parameters and grid adaptability gains to construct the total target input impedance. This step will reflect the virtual resistance, which reflects the thermal hysteresis characteristics. Virtual capacitance reflecting thermal capacitance characteristics And adaptive gain reflecting grid acceptance capacity. They are unified into a complex impedance model.

[0084] In physical implementation, this is equivalent to building a variable-parameter RC filter network on the DC side of the converter. In the complex frequency domain The following is the synthesis formula: ; In the formula, and These are the status parameters output by the preceding module; This is the inherent physical capacitance value of the DC side of the converter. (Introduced) The technical objective of this project is to provide a double safety net, both mathematically and physically: even with adaptive gain or virtual capacitor Because the operating condition abnormally approaches 0, the denominator still contains a non-zero constant. This ensures that the impedance expression will not have a singularity, and that the system characteristics smoothly degenerate into those of a conventional converter.

[0085] S404 and impedance synthesis module 104 discretize the synthesized impedance command and limit its rate of change to generate the final control command. Since the controller operates in the discrete digital domain, it is necessary to... Domain impedance model transformed into Domain difference equations.

[0086] This embodiment employs a bilinear transformation. Discretization is performed to ensure minimal frequency response distortion during the mapping process. Simultaneously, to prevent distortion of the synthesized impedance during grid fault recovery or SCR abrupt changes... A step change causes an overshoot in the converter's inner loop current. The module incorporates a rate-of-change limiter at the output to limit this overshoot. Maximum change per control cycle .

[0087] The impedance command after verification Ultimately, the signal is fed into the converter voltage loop control circuit, and the control system exhibits adaptive impedance characteristics that take into account both thermophysical constraints and grid stability requirements.

[0088] The control execution module 105 is located at the bottom layer of the control algorithm. It is responsible for converting the abstract impedance strategy and voltage command calculated by the upper layer into the actual on and off signals of the power electronic switching devices. In order to ensure that the energy router can accurately reproduce the strong network multi-resistance and weak network self-protection characteristics defined by the impedance synthesis module 104 within milliseconds, this module constructs a precise mapping mechanism from the complex domain to the time domain and combines it with feedforward decoupling technology to eliminate coupling interference between the d and q axes, thus ensuring the speed and independence of the adjustment.

[0089] S501, the control execution module 105 performs torque matrix calculations to convert the complex domain impedance to the time domain voltage correction. Based on the rotating coordinate theory, the virtual capacitance and virtual resistance characteristics in the complex domain are mapped to the synchronously rotating dq coordinate system. The module first calculates the virtual reactive mode value at the fundamental frequency. To prevent the denominator from being zero and to ensure that the calculated capacitive reactance reflects the cross-coupling strength in the fundamental coordinate system, the specific calculation logic is as follows: ; Subsequently, an impedance-corrected voltage vector is constructed. Given that capacitive impedance causes the voltage vector to lag behind the current vector in a rotating coordinate system (i.e., the d-axis current generates a negative voltage on the q-axis, and the q-axis current generates a positive voltage on the d-axis), the module uses the following correction matrix to reflect the physical characteristics of the virtual capacitor: ; In the formula, It is the real-time fundamental angular frequency output by the phase-locked loop of the power grid, used to decouple the coupling components introduced by the rotation of the coordinate system; This represents the grid-connected current component sampled at the current moment.

[0090] The physical meaning of this matrix operation is as follows: On the main diagonal This term characterizes the in-phase voltage drop generated by the virtual resistance, which plays a role in increasing the active power damping of the system; Secondary diagonal The term represents the orthogonal voltage coupling components generated by the virtual capacitor. Specifically, the positive sign in the first row and second column of the matrix ( ) and the negative sign in the second row and first column ( The phase relationship between the voltage vector and the current vector was established, thereby forcing the converter port to exhibit capacitive characteristics and correctly reproducing the virtual capacitor energy storage effect defined in S203.

[0091] S502. Based on the modified voltage command, the control execution module 105 runs a current inner-loop control algorithm that includes feedforward decoupling.

[0092] To address the dynamic coupling problem of d-q axis currents in high-power applications of LCL filters (i.e., adjusting the d-axis current causes q-axis oscillations), and to eliminate the influence of grid background harmonics on control accuracy, this embodiment employs a PI control structure combining feedforward decoupling and full grid voltage feedforward. The inner loop output reference voltage vector... The discretized calculation equations are as follows: ; The integral term is iteratively updated using the trapezoidal integration method to improve computational accuracy. ; In the formula, represent or axis; , These are the proportional and integral coefficients of the current loop, respectively. Frequency domain correction is used to adjust the current loop open-loop cutoff frequency to be equal to the switching frequency. (like To ensure speed; To control the cycle; This is the value of the physical filter inductance.

[0093] In the formula and The term is a decoupling term used to counteract the rotational electromotive force on the inductor; The term is the impedance correction term calculated in the previous step, which is the key interface for embedding virtual physical characteristics into electrical control.

[0094] S503, the control execution module 105 performs amplitude limiting and inverse coordinate transformation on the output reference voltage vector. This takes into account the DC bus voltage. Not only is it limited by the upstream thermal generator, but the inverter's linear modulation region also has a physical upper limit. If the command voltage exceeds the limit, it will cause the output waveform to be distorted and the integrator to saturate.

[0095] The module first calculates the reference voltage amplitude. And execute the following overmodulation protection logic: ; Here, the condition for entering the upper branch implies... It must be greater than a non-zero positive number. Therefore, the division operation is numerically safe. Subsequently, the real-time phase angle provided by the phase-locked loop is utilized. The inverse Parker transformation converts the DC command in the rotating coordinate system into the AC command in the two-phase stationary coordinate system. : ; This step uses a scaling factor. This ensures that the direction of the voltage vector remains unchanged (i.e., the phase information is preserved) when the voltage command exceeds the limit, and only the amplitude is truncated, thereby maintaining the synchronous stability of the system.

[0096] It should be noted that the physical implementation of the control execution module 105 is based on the physical thin-film capacitor or electrolytic capacitor configured on the DC side of the energy router.

[0097] During the initial extremely short instant of the transient process (microseconds to milliseconds), the physical capacitor provides instantaneous power throughput to maintain DC voltage stability; Subsequently, by adjusting the aforementioned virtual impedance control strategy, the control system changes the power throughput command of the converter, forcing a change in the power flow on the thermal interface side, thereby transferring the energy consumed by the physical capacitor to the thermal system on a time scale of milliseconds to seconds.

[0098] This relay mechanism, where a physical capacitor buffers transients and a thermodynamic system assumes steady-state conditions, ensures the enormous stability of the mathematical model. It is physically feasible.

[0099] S504 and the control execution module 105 use space vector pulse width modulation (SVPWM) technology to generate the final gate drive signal. Compared with traditional sinusoidal pulse width modulation, SVPWM technology can improve the DC voltage utilization by about 15.47% by injecting the third harmonic into the zero-sequence component, thereby outputting a higher AC voltage at the same DC bus voltage.

[0100] Module according to The location of the hexagonal sector is used to calculate the space vector of two adjacent basic voltages using the volt-second balance principle. and zero vector The duration of action of SVPWM. For the specific sector determination and time calculation of SVPWM, those skilled in the art can perform the task according to standard algorithms, and will not be elaborated upon here.

[0101] To prevent short circuits and burnout caused by shoot-through between the upper and lower transistors in the same bridge arm, the module inserts a dead time at the edge of the complementary signal transition when generating the six PWM waveforms. (Based on the IGBT trailing characteristic, the optimal value range is selected) Up to 4 The final six high-frequency pulse signals generated After being amplified by the optocoupler isolation drive circuit, the gate of the IGBT power module is controlled, driving the energy router to inject a current waveform into the power grid that meets the expected requirements.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-energy complementary energy router collaborative control and scheduling system, characterized in that, The energy router is equipped with a central controller. The central controller includes: The data acquisition module is used to collect electrical signals from the common connection point and thermal status signals from the local heating system in real time. The state construction module is used to calculate the effective power boundary within the transient control time window of the power grid based on the thermal state signal, and map the effective power boundary as a virtual reference capacitor on the DC side. At the same time, it maps the response hysteresis characteristics of the thermal system as a virtual resistance connected in series with the virtual reference capacitor to generate a complex domain impedance model. The intensity identification module is used to estimate the real-time short-circuit ratio of the external power grid; An impedance synthesis module is used to adjust the connection ratio of the virtual reference capacitor according to the real-time short-circuit ratio, and synthesize the total target input impedance in combination with the virtual resistor; The control execution module is used to generate a drive signal based on the total target input impedance to control the operation of the voltage source converter of the energy router.

2. The multi-energy complementary energy router collaborative control and scheduling system according to claim 1, characterized in that, The specific method by which the state construction module calculates the virtual reference capacitance is as follows: Based on the thermodynamic law of conservation of energy, calculate the energy storage capacity of the local thermal system within the safe operating temperature boundary; By introducing the step response time constant of the thermal system, the effective power boundary that can be converted within the transient control time window of the power grid is calculated. Based on the principle of capacitor energy storage, the capacitance value required to release energy equivalent to the effective power boundary within the allowable voltage fluctuation range of the DC bus is calculated and used as the virtual reference capacitor.

3. The multi-energy complementary energy router collaborative control and scheduling system according to claim 2, characterized in that, The state construction module determines the virtual resistance in the following way: Establish the inverse proportional relationship between the virtual resistance and the virtual reference capacitance, and the direct proportional relationship with the step response time constant; The virtual resistor and the virtual reference capacitor are configured to form a high-pass filter impedance characteristic, and the virtual resistor is used to limit the current mutation rate to simulate the physical response hysteresis of the thermodynamic system.

4. The multi-energy complementary energy router collaborative control and scheduling system according to claim 1, characterized in that, The intensity identification module executes the following logic: Calculate the rate of change norm of voltage and current at the point of common coupling as the electrical excitation level; The electrical excitation level is compared with a preset excitation threshold. If the electrical excitation level is lower than the excitation threshold, lock the previous time-estimation of the grid equivalent impedance. If the electrical excitation level is higher than or equal to the excitation threshold, the Thevenin equivalent parameters of the external power grid are updated using a recursive least squares algorithm, and then the real-time short-circuit ratio is calculated.

5. The multi-energy complementary energy router collaborative control and scheduling system according to claim 4, characterized in that, The recursive least squares algorithm in the intensity identification module uses an incremental linear regression equation to construct a linear mapping relationship between the electrical quantity fluctuations at the point of common coupling and the resistance and inductance parameters of the external power grid, and introduces a forgetting factor to iteratively update the parameters.

6. The multi-energy complementary energy router collaborative control and scheduling system according to claim 1, characterized in that, When calculating the total target input impedance, the impedance synthesis module uses an S-shaped nonlinear function to construct the mapping relationship between the real-time short-circuit ratio and the adaptive gain. When the real-time short-circuit ratio is higher than the mode switching threshold, the adaptive gain tends to the maximum value, increasing the access ratio of the virtual reference capacitor; When the real-time short-circuit ratio is lower than the mode switching threshold, the adaptive gain rapidly decays to the minimum retention value, reducing the access ratio of the virtual reference capacitor to prevent phase instability under weak network conditions.

7. A multi-energy complementary energy router collaborative control and scheduling system according to claim 1, characterized in that, The control execution module calculates the voltage correction amount generated by the total target input impedance in a synchronously rotating coordinate system, specifically including: Calculate the virtual capacitive reactance mode at the fundamental frequency; Construct an impedance correction matrix, wherein the main diagonal elements of the impedance correction matrix are the virtual resistances, and the secondary diagonal elements are the virtual capacitive reactance moduli. The impedance correction matrix is ​​used to calculate the current components in the synchronous rotating coordinate system to generate a voltage correction vector containing cross-coupling terms. The direct-axis voltage correction includes the positive coupling component generated by the quadrature-axis current, and the quadrature-axis voltage correction includes the negative coupling component generated by the direct-axis current, so as to reflect the capacitive impedance characteristics.

8. A multi-energy complementary energy router collaborative control and scheduling system according to claim 7, characterized in that, The control execution module also operates a current inner loop controller, which adopts a feedforward decoupling control structure. The control execution module superimposes the voltage correction vector as a negative feedback term onto the output of the current inner loop controller, and generates a reference voltage vector by combining it with the grid voltage feedforward.

9. A multi-energy complementary energy router collaborative control and scheduling system according to claim 1, characterized in that, The impedance synthesis module also includes an oscillation extraction unit based on the sliding discrete Fourier transform; The oscillation extraction unit is used to extract the low-frequency oscillation component of the common connection point voltage and lock the dominant oscillation frequency. The impedance synthesis module synthesizes the total target input impedance in a targeted manner according to the dominant oscillation frequency.

10. A multi-energy complementary energy router collaborative control and scheduling system according to claim 8, characterized in that, The control execution module performs modulation protection before generating the drive signal: Calculate the magnitude of the reference voltage vector; If the amplitude exceeds the upper limit of the linear modulation region corresponding to the DC bus voltage, a scaling factor is calculated to truncate the amplitude of the reference voltage vector while keeping the phase direction of the reference voltage vector unchanged.

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