An extended node power flow calculation method and system for supporting slow process simulation of a power system

Abstract

The application discloses an extended node power flow calculation method and system for supporting slow process simulation of a power system, and belongs to the technical field of power system analysis and control. The method comprises the following steps: acquiring system parameters of a target system, constructing a network admittance matrix based on the system parameters, and classifying nodes of the target system according to the network admittance matrix and device control characteristics; establishing constraint conditions for the classified nodes of the target system device, and taking flexible DC external sending power as a fixed boundary condition and incorporating the fixed boundary condition into an active power balance model of the target system; constructing an augmented nonlinear equation group based on the constraint conditions and the active power balance model, performing power flow calculation on the augmented nonlinear equation group, and outputting a power flow solution when a set convergence tolerance is met. The implementation of the application realizes decoupling of the phase reference function and the infinite active power balance function.

Description

Technical Field

[0001] This invention relates to the field of power system analysis and control technology, and more specifically, to an extended node power flow calculation method and system that supports slow process simulation of power systems. Background Technology

[0002] Currently, the construction of large-scale wind and solar power bases, focusing on desert, Gobi, and arid regions, is being accelerated. These bases are typically located at the end of the power grid, exhibiting a high proportion of power electronics in their power supply structure, and often require long-distance ultra-high-voltage direct current (UHVDC) transmission, especially flexible DC projects, to transmit power externally. The resulting "new energy island AC aggregation-DC transmission" system has significantly changed its physical characteristics and operation control methods compared to the traditional synchronous machine-dominated AC power grid.

[0003] In traditional power flow calculations, the system typically relies on a single balancing node to handle the active power imbalance across the entire network and simultaneously provide a phase angle reference for the entire network. This framework holds true in strong power grids dominated by synchronous machines, such as those from thermal and hydropower plants, because large synchronous machines with ample capacity can serve as both a phase reference and handle network losses and power deviations. However, in flexible DC islanded sending-end systems with a high proportion of renewable energy, the selection of traditional balancing nodes presents significant challenges.

[0004] Traditional power flow calculation methods cannot effectively handle high-proportion renewable energy islanded systems. Their fundamental flaw lies in the disconnect between the node model and physical reality. On one hand, the system lacks a true "balancing machine," and forcibly designating any device as a balancing node will produce erroneous results that violate its actual control capabilities or operational constraints. On the other hand, traditional models cannot accurately describe the constant power factor control characteristics of renewable energy power plants, and the commonly used "external" processing methods in engineering can compromise the numerical stability of Newton's method, easily leading to iterative oscillations or divergence under weak grid conditions, resulting in poor convergence. Summary of the Invention

[0005] To address the above problems, this invention proposes an extended node power flow calculation method to support slow process simulation of power systems, comprising: Obtain the system parameters of the target system, construct the network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics; For the target system equipment, nodes are classified and constraints are established. Flexible DC power transmission is incorporated as a fixed boundary condition into the active power balance model of the target system. Based on the aforementioned constraints and active power balance model, an augmented nonlinear equation system is constructed. Power flow solutions are then obtained from the augmented nonlinear equation system, and the power flow solution is output when the set convergence tolerance is met.

[0006] Optional system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

[0007] Optional, the target system nodes can be categorized as follows: PQ nodes, PV nodes, and Slack nodes; Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

[0008] Optionally, for the target system devices, introduce classified nodes and establish constraints, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

[0009] Optionally, for the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

[0010] Optionally, for the wind and solar power units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φ i The signified power factor angle.

[0011] Optional, an augmented nonlinear equation set, specifically: an augmented nonlinear equation set including conventional active residuals, conventional reactive residuals, and power factor coupling residuals; Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations Alternative The equation, formula is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

[0012] Optionally, power flow solutions can be obtained for the augmented nonlinear equations based on Newton's method or an equivalent iterative algorithm.

[0013] Optional power flow solution results include: voltage magnitude and power distribution at each node, and active and reactive power operation results at extended nodes.

[0014] Furthermore, this invention also proposes an extended node power flow calculation system to support slow process simulation of power systems, comprising: An initial unit is used to obtain the system parameters of the target system, construct a network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics. The modeling unit is used to introduce classified nodes for the target system equipment, establish constraints, and incorporate the flexible DC power transmission as a fixed boundary condition into the active power balance model of the target system. The solution unit is used to construct an augmented nonlinear equation system based on the constraints and the active power balance model, solve the power flow problem on the augmented nonlinear equation system, and output the power flow solution when the set convergence tolerance is met.

[0015] Optional system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

[0016] Optional, the target system nodes can be categorized as follows: PQ nodes, PV nodes, and Slack nodes; Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

[0017] Optionally, for the target system devices, introduce classified nodes and establish constraints, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

[0018] Optionally, for the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

[0019] Optionally, for the wind and solar power units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φ i The signified power factor angle.

[0020] Optional, an augmented nonlinear equation set, specifically: an augmented nonlinear equation set including conventional active residuals, conventional reactive residuals, and power factor coupling residuals; Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations Alternative The equation, formula is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

[0021] Optionally, power flow solutions can be obtained for the augmented nonlinear equations based on Newton's method or an equivalent iterative algorithm.

[0022] Optional power flow solution results include: voltage magnitude and power distribution at each node, and active and reactive power operation results at extended nodes.

[0023] In another aspect, the present invention also provides a computing device, comprising: one or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described above is implemented.

[0024] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described above.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides an extended node power flow calculation method to support slow process simulation of power systems, comprising: obtaining system parameters of the target system; constructing a network admittance matrix based on the system parameters; classifying the nodes of the target system according to the network admittance matrix and equipment control characteristics; establishing constraints for the classified nodes of the target system equipment, and incorporating flexible DC power transmission as a fixed boundary condition into the active power balance model of the target system; constructing an augmented nonlinear equation system based on the constraints and the active power balance model; solving the augmented nonlinear equation system for power flow; and outputting the power flow solution when a set convergence tolerance is met. The implementation of this invention decouples the phase reference function from the infinite active power balance function. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a structural diagram of a photovoltaic base connected to a flexible DC power transmission system, as described in an embodiment of the method of the present invention. Figure 3 A comparison diagram of the residual convergence process of two power flow iteration strategies in an embodiment of the present invention; Figure 4 This is a structural diagram of the system of the present invention. Detailed Implementation

[0027] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0028] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0029] Example 1: This invention proposes an extended nodal power flow calculation method S100 to support slow process simulation of power systems, such as... Figure 1 As shown, it includes: S101, Obtain the system parameters of the target system, construct the network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics; S102, for the target system equipment, introduce classified nodes, establish constraints, and incorporate flexible DC power transmission as a fixed boundary condition into the active power balance model of the target system; S103. Based on the constraints and active power balance model, construct an augmented nonlinear equation system, solve the power flow problem on the augmented nonlinear equation system, and output the power flow solution when the set convergence tolerance is met.

[0030] The system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

[0031] The target system nodes are classified as follows: PQ nodes, PV nodes, and Slack nodes. Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

[0032] Specifically, for the nodes categorized for the target system equipment, constraints are established, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

[0033] Specifically, for the synchronous condenser equipment and grid-type energy storage equipment in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

[0034] Specifically, for the wind power and photovoltaic units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φ i The signified power factor angle.

[0035] Among them, the augmented nonlinear equations specifically include the augmented nonlinear equations of conventional active residuals, conventional reactive residuals, and power factor coupling residuals. Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations Alternative The equation, formula is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

[0036] Among them, the augmented nonlinear equations are solved by power flow based on Newton's method or equivalent iterative algorithm.

[0037] The power flow solution results include: voltage amplitude and power distribution at each node, as well as the active and reactive power operation results of the extended nodes.

[0038] The invention will be further illustrated below with specific examples: This invention constructs Q-θ nodes and cosφ-θ nodes to form an extended node system adapted to the operation of new power systems. Furthermore, it directly embeds the power factor coupling constraint into the Newton power flow equation and the Jacobian matrix to achieve a unified solution for the strong active-reactive coupling characteristics of new energy power plants. Finally, under a given DC transmission plan boundary, it obtains a physically consistent, convergent, robust power flow solution applicable to weak network conditions. This provides convergent initial power flow values ​​for slow process simulation in time-domain simulation technology, or provides a power flow solution method for continuous power flow technology.

[0039] The specific implementation steps include: Step 1. Obtain the bus parameters, branch parameters, and ground-to-ground branch parameters of the target system, and establish the network admittance matrix; Step 2. Classify the nodes according to their control characteristics, including at least Slack nodes, PQ nodes, and PV nodes; (1) The known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; (2) The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ; (3) The known quantities corresponding to the Slack nodes are V and θ, and the unknown quantities are P and Q.

[0040] Step 3: For synchronous condensers, grid-type energy storage, and other equipment in the sending-end islanded system, introduce the Qθ node, a first-type extended node. Given the reference phase angle θ and the reactive power target Q, the node voltage is solved by the power flow equation, and the active power injection of the node is used as the result for back calculation.

[0041] For the Qθ node, at the NR state variable level, the main problem to solve is its voltage magnitude; the active power injection at the node is calculated from the converged power flow equations.

[0042] (1) Reactive power balance equation, used to solve The standard reactive power mismatch equation is retained and incorporated into the Jacobian matrix to solve for the nodal voltage magnitude. Let the node... The injected reactive power calculation value is The residual equation is then: (1) Where r and j are node numbers, For reference phase angle, Grj and Brj are elements of the network admittance matrix.

[0043] (2) The active power balance equation serves as the basis for complementary constraints. The active power calculation function for node Qθ is defined as follows: (2) (3) The active power balance relationship of node Qθ is retained, but it is not interpreted as unlimited balanced power. Instead, it is combined with the capacity boundary and global relaxation variables to construct the following constraints: (3) In the formula, Let be the active balance residual at node r of Qθ containing the global slack variable λ. This represents the calculated value of the active power injected at node r of voltage θ under the current voltage magnitude and phase angle. The target or corrected active power is represented by the capacity boundary and the global relaxation variable λ; V is the node voltage magnitude vector, θ is the node voltage phase angle vector, and λ is the global relaxation variable. Therefore, the Qθ node in the model assumes the role of "phase reference + finite active power buffer": when its active power result is within the allowable range, it can absorb small imbalances; when it exceeds the boundary, the remaining imbalance is distributed among adjustable resources by subsequent distributed relaxation, and the phase reference function of the Qθ node remains unchanged. This is achieved by setting an active power over-limit correction amount. The dead zone can be degenerated into an unrestricted balanced node.

[0044] Step 4: For wind and solar power units, construct cosφ-θ coupled nodes, type II extended nodes, given a reference phase angle and power factor angle, the nodes always satisfy Q=Ptanφ. Here, Q is not an independently set quantity, but is determined in real time by P through the power factor relationship.

[0045] (4) In the formula, Let be the power factor constraint function for the i-th cosφ-θ coupled node. , Let represent the active and reactive power injected at node i, respectively. γi is the active-reactive coupling coefficient derived from the target power factor at node i. This constraint covers the entire operating range from inductive to capacitive. When cosφ=1, γi=0, degenerating into the constant reactive power Q=0 mode.

[0046] Step 5. Incorporate the flexible DC power transmission as a fixed boundary condition into the system's active power balance model; Step 6. Construct an augmented nonlinear equation system that includes the conventional active power residual, the conventional reactive power residual, and the power factor coupling residual; linearize the equation system and construct an augmented Jacobian matrix that includes coupling derivative terms; (1) Establishing the coupled residual equation: Constructing a new nodal unbalance equation Replace traditional equation: (5) (2) Derivation of the Jacobian matrix: For Perform total differential linearization. The basic Jacobian subblock is: (6) and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. , Let represent the active and reactive power injected at node i, respectively. Let be the voltage magnitude at node i. According to the chain rule, the modified Jacobian elements should include... and The derivative contributions of the two parts: (7) (3) Modified matrix structure: Jacobian matrix of the embedded method It becomes: (8) In the formula, is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. For each cosφ-θ coupled node The resulting diagonal coupling matrix.

[0047] (4) Constructing and solving the augmented Jacobian matrix system: Based on the extended node model (Q0 node, PV node with coupling constraints, etc.) established in steps (2) and (3), an augmented nonlinear equation system including conventional active residual, conventional reactive residual and power factor coupling residual is constructed. Step 7. Use Newton's method or equivalent iterative algorithm to solve for the voltage magnitude, phase angle, and related results of the extended nodes at each node; Step 8. When the set convergence tolerance is met, output the power flow solution, including the voltage and power distribution of each node and the active and reactive power operation results of the extended nodes.

[0048] The following uses a large photovoltaic base in western China as an example to verify the invention, including: Adopting the planning scheme of a large-scale photovoltaic base in western China via a flexible DC transmission system, the power grid outside the flexible DC access nodes is equivalently processed to form an islanded transmission system with 38 nodes and 45 branches. This system includes 9 photovoltaic generators and 16 transformers, covering multiple voltage levels such as 230kV, 37kV, and 0.4kV. The total system load is 610MW, and the DC transmission power is 1300MW. The equivalent grid wiring diagram is shown below. Figure 2 As shown.

[0049] To facilitate the drawing of geographical connection diagrams, Gen1, Bus8, Bus11, etc. are nodes formed by merging several adjacent stations.

[0050] (1) Description of balancing node setting: Traditional power flow algorithms set Gen1 as the balancing node. In the node extension type algorithm of this paper, Gen1 is selected as the cosφ-θ node, and constant power factor control is adopted (assuming...). ), replacing the traditional balance node.

[0051] (2) Description of PV and PQ node types: The node types are set according to the traditional power flow calculation. The 37kV collection bus of the three photovoltaic power stations, Gen2 / 3 / 5, are equipped with reactive power compensation devices and are set as PV nodes with active power of 0; the remaining 34 nodes are PQ nodes.

[0052] (3) DC transmission channel: Connect to the rectifier side of the VSC high-voltage DC transmission system at Bus1 node, with the initial transmission power set as follows: According to the PQ node calculation, the reactive power is 0.

[0053] Verification of the effectiveness of the reactive power servo mechanism: Compared with traditional methods, this invention presents the computational differences between the external modeling method and the recommended embedded modeling method when a constant power factor power station undertakes balancing tasks. All three methods select... Figure 2 In the photovoltaic power station, Gen1 is used as the active power relaxation node (Vθ or cosφ-θ node), while the remaining 37 nodes maintain the same type. Under the premise of maintaining constant power factor control, calculations were performed using traditional slack node processing, the external interactive processing presented in this paper, and the cosφ-θ embedded processing, respectively. The results are as follows: (1) The traditional power flow method ignores the reactive power constraint of the Gen1 power station. Although the active power bears the unbalanced power of the system, the active power output of this node is 89.7MW, but the reactive power remains at the initial planned value (38.8 Mvar). This causes the actual power factor to drift to 0.9178, which violates the grid connection control requirements. If the power factor is adjusted to 0.95, it requires repeated manual attempts, which is time-consuming and has low calculation efficiency.

[0054] (2) To compare the convergence characteristics of the external and internal methods proposed in this paper under different system strengths, three scenarios, S1, S2, and S3, were constructed by adjusting the impedance of the AC 220kV grid lines, while keeping the equipment parameters, control methods, operating methods, and initial values ​​consistent. The multi-site short-circuit ratio (MRSCR) of the balancing machine corresponding to the three scenarios was calibrated to 3.5, 2.5, and 1.5, respectively, representing strong, medium-weak, and extremely weak network conditions, and was used to examine the differences in algorithm convergence when the system changes from strong to weak.

[0055] 1. S1: MRSCR = 3.5, corresponding to strong network operating conditions; 2. S2: MRSCR = 2.5, corresponding to medium-weak network conditions; 3. S3: MRSCR = 1.5, corresponding to extremely weak network conditions.

[0056] (3) The calculation results are as follows Figure 3As shown, the embedded coupled modeling integrates the power factor coupling equation and the active power balance equation with slack variables into a unified solution using the augmented Jacobian matrix, which naturally satisfies the condition at the convergence point. The reactive power output can coordinate with the active power output and strictly maintain the power factor, exhibiting good convergence stability in all scenarios. In contrast, while the external interactive modeling can still converge in scenarios S1 and S2, the number of iterations increases to about 15, and max(|ΔP|,|ΔQ|) rebounds between the 2nd and 4th iterations, showing significant fluctuations. When the system is further weakened to S3, its convergence deteriorates further, making it difficult to maintain stable convergence.

[0057] This invention decouples the phase reference function from the unlimited active power balancing function. By constructing the Qθ node, the traditional synchronous balancing node can be replaced by devices that actually perform the phase anchoring function, such as synchronous condensers and network-type equipment, which is more in line with the actual physical characteristics of islanded sending-end systems.

[0058] This invention achieves unified embedded modeling of constant power factor renewable energy units. By constructing cosφ-θ nodes and directly incorporating coupling constraints into the power flow equations, it avoids the repeated external corrections caused by traditional external interactive methods.

[0059] This invention improves the convergence stability of power flow calculations under weak network conditions. Because the active and reactive power coupling derivative terms are explicitly preserved in the Jacobian matrix, the algorithm's search direction is closer to the tangent direction of the real nonlinear manifold, which can significantly reduce the risk of numerical oscillations and divergence under weak network conditions.

[0060] Example 2: Furthermore, this invention also proposes an extended node power flow calculation system 200 to support slow process simulation of power systems, such as... Figure 4 As shown, it includes: The initial unit 201 is used to obtain the system parameters of the target system, construct a network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics. Modeling unit 202 is used to introduce classified nodes for the target system equipment, establish constraints, and incorporate flexible DC power transmission as a fixed boundary condition into the active power balance model of the target system. The solver unit 203 is used to construct an augmented nonlinear equation system based on the constraints and the active power balance model, solve the power flow problem on the augmented nonlinear equation system, and output the power flow solution when the set convergence tolerance is met.

[0061] The system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

[0062] The target system nodes are classified as follows: PQ nodes, PV nodes, and Slack nodes. Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

[0063] Specifically, for the nodes categorized for the target system equipment, constraints are established, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

[0064] Specifically, for the synchronous condenser equipment and grid-type energy storage equipment in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

[0065] Specifically, for the wind power and photovoltaic units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φi The signified power factor angle.

[0066] Among them, the augmented nonlinear equations specifically include the augmented nonlinear equations of conventional active residuals, conventional reactive residuals, and power factor coupling residuals. Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations The alternative to the traditional ΔQ equation is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

[0067] Among them, the augmented nonlinear equations are solved by power flow based on Newton's method or equivalent iterative algorithm.

[0068] The power flow solution results include: voltage amplitude and power distribution at each node, as well as the active and reactive power operation results of the extended nodes.

[0069] The implementation of this invention achieves the decoupling of the phase reference function and the infinite active power balance function.

[0070] Example 3: Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby implementing the steps of the methods in the above embodiments.

[0071] Example 4: Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiments.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0077] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An extended node power flow calculation method supporting slow process simulation of power systems, characterized in that, include: Obtain the system parameters of the target system, construct the network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics; For the target system equipment, nodes are classified and constraints are established. Flexible DC power transmission is incorporated as a fixed boundary condition into the active power balance model of the target system. Based on the aforementioned constraints and active power balance model, an augmented nonlinear equation system is constructed. Power flow solutions are then obtained from the augmented nonlinear equation system, and the power flow solution is output when the set convergence tolerance is met.

2. The extended node power flow calculation method according to claim 1, characterized in that, The system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

3. The extended node power flow calculation method according to claim 1, characterized in that, The target system nodes are classified as follows: PQ nodes, PV nodes, and Slack nodes; Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

4. The extended node power flow calculation method according to claim 1, characterized in that, For the nodes of the target system equipment, introduce classification and establish constraints, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

5. The extended node power flow calculation method according to claim 4, characterized in that, For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

6. The extended node power flow calculation method according to claim 4, characterized in that, For the wind and solar power units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φ i The signified power factor angle.

7. The extended node power flow calculation method according to claim 1, characterized in that, The augmented nonlinear equations are specifically: an augmented nonlinear equations including conventional active residuals, conventional reactive residuals, and power factor coupling residuals; Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations Alternative The equation, formula is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

8. The extended node power flow calculation method according to claim 1, characterized in that, Power flow solutions are obtained for augmented nonlinear equations based on Newton's method or equivalent iterative algorithms.

9. The extended node power flow calculation method according to claim 8, characterized in that, The power flow solution results include: voltage amplitude and power distribution at each node, as well as the active and reactive power operation results of the extended nodes.

10. An extended node power flow calculation system supporting slow process simulation of power systems, characterized in that, include: An initial unit is used to obtain the system parameters of the target system, construct a network admittance matrix based on the system parameters, and classify the nodes of the target system according to the network admittance matrix and the device control characteristics. The modeling unit is used to introduce classified nodes for the target system equipment, establish constraints, and incorporate the flexible DC power transmission as a fixed boundary condition into the active power balance model of the target system. The solution unit is used to construct an augmented nonlinear equation system based on the constraints and the active power balance model, solve the power flow problem on the augmented nonlinear equation system, and output the power flow solution when the set convergence tolerance is met.

11. The extended node power flow calculation system according to claim 10, characterized in that, The system parameters include: bus parameters, branch parameters, and ground-connected branch parameters.

12. The extended node power flow calculation system according to claim 10, characterized in that, The target system nodes are classified as follows: PQ nodes, PV nodes, and Slack nodes; Among them, the known quantities corresponding to nodes P and Q are P and Q, and the unknown quantities are V and θ; The known quantities corresponding to the PV nodes are P and V, and the unknown quantities are Q and θ. The known quantities for each Slack node are V and θ, and the unknown quantities are P and Q.

13. The extended node power flow calculation system according to claim 10, characterized in that, For the nodes of the target system equipment, introduce classification and establish constraints, including: For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced and constraints are constructed. For the wind power and photovoltaic units in the target system, constraints are constructed by building cosφ-θ coupled nodes.

14. The extended node power flow calculation system according to claim 13, characterized in that, For the synchronous condenser and grid-type energy storage devices in the target system, Qθ nodes are introduced, and the constraints are as follows: in, For Q Node r contains global slack variables The active power balance residual; The node voltage magnitude vector. The node voltage phase angle vector. A global slack variable used to characterize the unbalanced active power to be allocated. In the current , Q is calculated based on the network admittance matrix. Calculated active power injection at node r For Q Node r in global slack variables The given active power or boundary-corrected active power under the action.

15. The extended node power flow calculation system according to claim 13, characterized in that, For the wind and solar power units in the target system, the constraints are constructed by building cosφ-θ coupled nodes as follows: in, Let be the power factor constraint function for the i-th cosφ-θ coupled node; Inject active power into node i; Inject reactive power into node i; The active-reactive coupling coefficient is obtained by converting the target power factor of node i. =tanφ i , φ i The signified power factor angle.

16. The extended node power flow calculation system according to claim 10, characterized in that, The augmented nonlinear equations are specifically: an augmented nonlinear equations including conventional active residuals, conventional reactive residuals, and power factor coupling residuals; Construct an augmented nonlinear system of equations, including: Establish the coupled residual equations, including: Construct new nodal imbalance equations Alternative The equation, formula is as follows: Perform the derivation of the Jacobian matrix, where, for After performing total differential linearization, the basic Jacobian subblock is obtained, as shown in the following formula: After correcting the Jacobian matrix, we get: in, and These characterize the sensitivity of voltage amplitude changes to active and reactive power, respectively. The power factor coupling residual of the i-th cosφ-θ coupling node; and These are the calculated values ​​of reactive and active power injected into node i, respectively, under the current voltage amplitude vector V and voltage phase angle vector θ. , These represent the active and reactive power injected at node i, respectively; Vi represents the voltage amplitude at node i. is the augmented Jacobian matrix after embedding power factor coupling constraints; H, N, M, and L are the active-phase angle, active-voltage, reactive-phase angle, and reactive-voltage partial derivative submatrices in the traditional power flow Jacobian matrix, respectively. Let be the active-reactive coupling coefficient of node i. For each The resulting diagonal coupling matrix.

17. The extended node power flow calculation system according to claim 10, characterized in that, Power flow solutions are obtained for augmented nonlinear equations based on Newton's method or equivalent iterative algorithms.

18. The extended node power flow calculation system according to claim 17, characterized in that, The power flow solution results include: voltage amplitude and power distribution at each node, as well as the active and reactive power operation results of the extended nodes.

19. A computer device, characterized in that, include: One or more processors; A processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method described in any one of claims 1-9 is implemented.

20. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the method as described in any one of claims 1-9.

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