Optimized scheduling method and system for flexible interconnected power distribution network

Abstract

The invention discloses an optimal scheduling method for a flexible interconnected power distribution network. The method comprises the following steps: establishing a small-interference stability slope optimization model of a voltage source converter; optimizing the small interference stability slope optimization model by using a mode of combining a sequence interior point method and a genetic algorithm to obtain an optimized model; taking a source load power expectation scene set in consideration of a vertical reference point and a scheduling interval as scheduling strategy input, taking the total network loss for minimizing a plurality of expectation scenes as an optimization setting target of an optimized model, optimizing the droop slope of a voltage source converter according to the target and an input condition, and constraining the power flow of a direct current distribution network to obtain the optimal power flow of the direct current distribution network. Determining an actual scheduling strategy and an actual constraint of the optimized model by taking the power constraint of the voltage source converter and the processed small interference stability constraint as constraint conditions, and obtaining a target optimized scheduling model; and scheduling each component of the flexible interconnection power distribution network by using the target optimization scheduling model.

Description

Technical Field

[0001] The present application relates to the field of dispatch optimization, and in particular to an optimized dispatch method, device, electronic device and computer-readable storage medium for a flexible interconnected distribution network. Background Art

[0002] As the proportion of distributed energy (such as solar energy and wind energy) in the power grid increases, the operation of the power grid becomes more complex and dynamic. The volatility and uncertainty of renewable energy have posed new challenges to the stability of the power grid, especially the dynamic response of the power grid under small disturbances. Modern power grids need to be able to respond quickly and effectively to various small disturbances (such as load changes, energy output fluctuations, etc.) to maintain stable operation.

[0003] In the case of a high proportion of renewable energy access, it becomes more important to maintain the stability of the power grid, especially the stability of small disturbances. As a key device connecting distributed energy and the power grid, the voltage source converter (VSC) has an important impact on the stability of the power grid, and the control strategy of the VSC, especially the droop control strategy, is crucial to improving the responsiveness of the power grid to small disturbances. Therefore, the traditional power grid stability analysis and control strategy may no longer be applicable to the power grid environment with a high proportion of distributed and renewable energy. Summary of the invention

[0004] The purpose of this application is to provide a method, device, electronic device and computer-readable storage medium for optimizing scheduling of a flexible interconnected distribution network.

[0005] To achieve the above objectives, the present application provides, in a first aspect, a method for optimizing scheduling of a flexible interconnected distribution network, comprising:

[0006] A voltage source converter small disturbance stability slope optimization model is established by taking a preset index as a target of an initial model and adding a linearized small disturbance stability constraint to the initial model; wherein the preset index includes a first index of a distribution network small disturbance stability margin represented by a distance from a real part of a dominant eigenvalue to an imaginary axis;

[0007] The voltage source converter small disturbance stability slope optimization model is optimized by combining a sequential interior point method with a genetic algorithm to obtain an optimized model;

[0008] Taking into account the vertical reference point and the expected source-load power scenario set within the dispatching interval as the dispatching strategy input, minimizing the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimizing the droop slope of the voltage source converter according to the target and input conditions, taking the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions, determining the actual dispatching strategy and actual constraints of the optimized model, and obtaining the target optimization dispatching model;

[0009] The target optimization scheduling model is used to schedule the various components of the flexible interconnected distribution network.

[0010] The preset indicators also include:

[0011] A second indicator corresponding to the eigenvalue: calculating the eigenvalue of a small signal state matrix of the power grid system to which the flexible interconnected distribution network belongs, the eigenvalue representing a dynamic response of the state of the system;

[0012] And a third indicator corresponding to stability: the stability of the power grid system is evaluated according to the size and position of the real part of the dominant eigenvalue, and the closer the real part of the dominant eigenvalue is to the imaginary axis, the higher the stability of the power grid system is.

[0013] The small disturbance stability constraint after linearization is determined by the following steps:

[0014] Predefined normal operation scenarios;

[0015] Performing power system flow calculation for each of the normal operating scenarios;

[0016] Performing stability analysis on the normal operation scenario completed by the power system power flow calculation to obtain a target scenario that meets the stability requirements;

[0017] Taking the constraint conditions of each target scene as initial small disturbance stability constraints;

[0018] The initial small disturbance stability constraint is linearized by applying matrix perturbation theory to obtain a linearized small disturbance stability constraint that can be added to the initial model.

[0019] The performing power system flow calculation for each of the normal operating scenarios includes:

[0020] Initializing the system: determining the load and power generation conditions of the system, including the active and reactive loads of each node, and the active and reactive outputs of the generator; specifying initial voltage estimates for all nodes;

[0021] Calculating power imbalance: For each node, calculating a power imbalance amount, wherein the power imbalance amount refers to the difference between the actual injected power and the expected injected power;

[0022] Constructing a Jacobian matrix used in the Newton-Raphson iteration method to describe the nonlinear behavior of the system, wherein the Jacobian matrix is ​​a partial derivative matrix of the nonlinear equations of the system;

[0023] The power imbalance and the Jacobian matrix are used to construct a linear equation system according to the Newton-Raphson iteration method: ΔV = -J -1 ΔP, where ΔV is the correction value of voltage amplitude and phase angle, J -1 is the inverse matrix of the Jacobian matrix, ΔP is the vector of power imbalances;

[0024] Update node voltages: Solve the linear equations and use the solution to update the voltage magnitude at each node and phase angle for: Where, ΔV i and Δθ i are the correction values ​​of voltage amplitude and phase angle respectively; the power imbalance value of each node is recalculated using the updated voltage amplitude and phase angle; the recalculated power imbalance value is used as the input of the next iteration;

[0025] Convergence judgment: Check whether the power imbalance is less than a predetermined tolerance limit. If so, it is determined that convergence and power flow calculation are completed; otherwise, the calculation steps of solving the linear equations are repeated to continue iteration.

[0026] The stability analysis of the normal operation scenario completed by the power system flow calculation to obtain a target scenario that meets the stability requirements includes:

[0027] The characteristic value of each normal operating scenario completed by the power system flow calculation is calculated respectively, and when the real part of the characteristic value is negative, it is determined that the corresponding normal operating scenario meets the stability condition, so as to obtain the target scenario.

[0028] The matrix perturbation theory is used to linearize the initial small disturbance stability constraints, including:

[0029] The calculation process of the initial small disturbance stability constraint is simplified by using the matrix perturbation theory to obtain a simplified constraint, wherein the matrix perturbation theory is used to analyze the change of the eigenvalue of the state matrix when the parameters of the power grid system change slightly;

[0030] Converting the simplified constraint into a linear or quasi-linear form to obtain a converted constraint;

[0031] An optimization algorithm including an interior point method and a gradient method is selected to optimize the post-conversion constraints to obtain a final small disturbance stability constraint that can be actually added to the initial model. The optimization algorithm is adapted to the model characteristics of the voltage source converter small disturbance stability slope optimization model.

[0032] The voltage source converter small disturbance stability slope optimization model is optimized by combining the sequential interior point method and the genetic algorithm to obtain an optimized model, which specifically includes:

[0033] The upper layer uses the genetic algorithm to randomly generate an initial population, and the lower layer uses the sequence interior point method to iteratively optimize the individuals in the population, and adds a characteristic value verification step in the solution process of each iterative optimization.

[0034] To achieve the above-mentioned object, the present application provides, in a second aspect, an optimization dispatching system for a flexible interconnected distribution network, characterized in that it includes:

[0035] The first unit is used to establish a small disturbance stability slope optimization model of a voltage source converter by taking a preset index as a target of an initial model and adding a small disturbance stability constraint after linearization processing by applying matrix perturbation theory under an expected scenario and an extreme source-load scenario to the initial model;

[0036] The second unit is used to optimize the voltage source converter small disturbance stability slope optimization model by combining the sequential interior point method and the genetic algorithm to obtain an optimized model;

[0037] The third unit is used to consider the vertical reference point and the source-load power expected scenario set within the scheduling interval as the scheduling strategy input, minimize the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimize the voltage source converter droop slope according to the target and input conditions, and use the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions to determine the actual scheduling strategy and actual constraints of the optimized model to obtain the target optimization scheduling model;

[0038] The fourth unit is used to dispatch the components of the flexible interconnected distribution network using the target optimization scheduling model.

[0039] To achieve the above-mentioned purpose, the present application provides an electronic device in a third aspect, the electronic device comprising:

[0040] Memory for storing computer programs;

[0041] A processor is used to implement the steps of the method for optimizing scheduling of a flexible interconnected distribution network as described in any embodiment of the first aspect when executing a computer program stored in a memory.

[0042] To achieve the above-mentioned objectives, the present application provides in a fourth aspect a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the steps of optimizing the scheduling of the flexible interconnected distribution network as described in any embodiment of the first aspect above.

[0043] The optimized scheduling scheme for the flexible interconnected distribution network provided in the present application constructs a voltage source converter small disturbance stability slope optimization model, and uses the voltage source converter small disturbance stability slope optimization model to reasonably and accurately schedule the various components of the flexible interconnected distribution network, thereby optimizing the grid operation efficiency on the basis of achieving effective scheduling of the power system while ensuring system stability. This scheme takes into account multiple scenarios and multiple constraints, and is more suitable for complex and changeable modern power systems.

[0044] The present application also provides an optimization scheduling device, electronic device and computer-readable storage medium for a flexible interconnected distribution network, which have the above-mentioned beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0046] Figure 1 A flowchart of an optimization scheduling method for a flexible interconnected distribution network provided in an embodiment of the present application;

[0047] Figure 2 is a structural schematic diagram of an AC / DC hybrid high-quality power distribution system based on multi-terminal flexible interconnection technology as an example;

[0048] Figure 3 It is a structural schematic diagram of a two-level voltage source converter;

[0049] Figure 4 is a structural schematic diagram of a three-level voltage source converter;

[0050] Figure 5 A flow chart of a method for obtaining a small disturbance stability constraint with an added input in the optimization scheduling method for a flexible interconnected distribution network provided in an embodiment of the present application;

[0051] Figure 6A flow chart of a method for calculating power system flow for a normal operating scenario in an optimization scheduling method for a flexible interconnected distribution network provided in an embodiment of the present application;

[0052] Figure 7 A structural block diagram of an optimization dispatching system for a flexible interconnected distribution network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] See also Figure 1 , Figure 1 A flowchart of an optimized scheduling of a flexible interconnected distribution network provided in an embodiment of the present application includes the following steps:

[0055] Step 101: Establishing a small disturbance stability slope optimization model for a voltage source converter by taking a preset index as a target of an initial model and adding small disturbance stability constraints linearized by applying matrix perturbation theory in expected scenarios and extreme source-load scenarios to the initial model;

[0056] This step is intended to establish a voltage source converter small disturbance stability slope optimization model by using pre-set objectives, constraints, and linearization processing of the initial model represented by preset indicators by an executor (such as a local server or cloud server for data processing and analysis) that is suitable for executing the optimization scheduling method for the flexible interconnected distribution network provided in this application.

[0057] Among them, the goal of the model may include preset indicators, which may include: defining the distance from the real part of the dominant eigenvalue to the imaginary axis as the first indicator of the small disturbance stability margin of the distribution network, the first indicator is used to reflect the response capability of the power grid system to which the flexible interconnected distribution network belongs to small disturbances, and the speed at which the power grid system recovers to a stable state. In simple terms, it is to minimize the total loss of the system or optimize specific performance indicators, and at the same time, it is necessary to ensure the stability of the system in extreme scenarios. Furthermore, it may also include a second indicator corresponding to the eigenvalue, that is, calculating the eigenvalue of the small signal state matrix in the power grid system, the eigenvalue represents the dynamic response of the state of the system, characterizes the situation of the eigenvalue, and a third indicator corresponding to stability, that is, evaluating the stability of the power grid system according to the size and position of the real part of the dominant eigenvalue, the closer the real part of the dominant eigenvalue is to the imaginary axis, the higher the stability of the power grid system, to characterize the stability.

[0058] In the stability analysis of power systems, the dynamic behavior of the system can be represented by a state space model. The general form of the state space model is x˙=Ax+Bu, where x is the system state vector and A is the system matrix. The eigenvalue of the system matrix A describes the response of the system to small disturbances. The real part of the eigenvalue reflects the attenuation rate of the system state to the disturbance. The larger the real part, the faster the system attenuates the disturbance and the better the stability.

[0059] The real part of the dominant eigenvalue and the stability margin: The dominant eigenvalue refers to the eigenvalue with the largest real part, denoted as λmax. The distance from the real part of the dominant eigenvalue to the imaginary axis can be expressed as Re(λmax), that is, the value of the real part of the eigenvalue. This distance can be used as an indicator of the system's small disturbance stability margin. In theory, Re(λmax) should be a negative value. The larger its absolute value, the better the system's stability to small disturbances.

[0060] Mathematical formula indicates: Let A be the system matrix, then the eigenvalue λ of the system can be obtained by solving the characteristic equation det(A-λI)=0, where I is the unit matrix. The real part Re(λmax) of the dominant eigenvalue λmax can be obtained by solving the above characteristic equation and extracting the eigenvalue with the largest real part.

[0061] Application of stability margin: The stability margin Re(λmax) can be used to evaluate the stability of the power grid and guide the setting of the VSC droop control strategy. In the optimization of the VSC control strategy, the stability margin can be used as a constraint to ensure that the stability of the power grid will not be reduced during the optimization process.

[0062] Specifically, the second index representing the characteristic value can be calculated by the following steps:

[0063] 1) Establish a small signal model

[0064] The nonlinear system is linearized at the operating point to form a linearized state space model:

[0065] Δx˙=AΔx+Bδu, Δy=CΔx+DΔu;

[0066] Among them, x is the state vector of the system, y is the output vector, u is the input vector, A is the state matrix, B is the input matrix, C is the output matrix, and D is the feedforward matrix.

[0067] 2) Eigenvalue analysis

[0068] Solve for the eigenvalues ​​of the state matrix A:

[0069] det(A-λI)=0;

[0070] Where λ represents the eigenvalue and I is the unit matrix. The real part of the eigenvalue determines the stability of the system. A negative real part indicates a stable system, while a positive real part indicates an unstable system.

[0071] Based on the obtained eigenvalues, the dynamic response characteristics of the system can be analyzed. The real and imaginary parts of the eigenvalues ​​determine the stability and oscillation frequency of the system respectively. If the real parts of all eigenvalues ​​are negative, the system is stable; if there are eigenvalues ​​with positive real parts, the system is unstable.

[0072] Through the above steps, the eigenvalues ​​of the small signal state matrix of the flexible interconnected distribution network can be calculated, and then the dynamic response of the system state can be analyzed.

[0073] Step 102: optimizing the small disturbance stability slope optimization model of the voltage source converter by combining the sequential interior point method with the genetic algorithm to obtain an optimized model;

[0074] On the basis of step 101, this step aims to optimize the small disturbance stability slope optimization model of the voltage source converter by the above-mentioned execution subject using a combination of the sequence interior point method and the genetic algorithm. Specifically, the upper layer uses the genetic algorithm to randomly generate the initial population, and the lower layer uses the sequence interior point method to iteratively optimize the individuals in the population, and adds an eigenvalue verification step in the solution process of each iterative optimization to ensure the accuracy of the optimization result and the reliability of the algorithm. For example, in each iteration process, the optimization result is checked for eigenvalues ​​to ensure that the stability of the system is not destroyed. If the eigenvalue verification fails (that is, the system becomes unstable), the parameters are adjusted or a penalty term is added to the objective function.

[0075] Step 103: taking the expected scenario set of source-load power within the vertical reference point and the dispatching interval as the dispatching strategy input, taking minimizing the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimizing the droop slope of the voltage source converter according to the target and input conditions, taking the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions, determining the actual dispatching strategy and actual constraints of the post-model, and obtaining the target optimization dispatching model;

[0076] Step 104: Use the target optimization scheduling model to schedule the components of the flexible interconnected distribution network.

[0077] Figure 2 The structure diagram of a high-quality AC / DC hybrid power distribution system based on multi-terminal flexible interconnection technology existing in the prior art is shown as follows: Figure 2 As shown, it includes a first medium-voltage AC circuit breaker, a second medium-voltage AC circuit breaker, an AC interconnecting switch, a first medium-voltage AC bus, a second medium-voltage AC bus, a first three-port AC / DC flexible interconnection switch, a second three-port AC / DC flexible interconnection switch, a first medium-voltage DC circuit breaker, a second medium-voltage DC circuit breaker, a medium-voltage DC bus, an energy storage system, a first flexible DC switch, a low-voltage DC circuit breaker, a low-voltage DC bus, a second flexible DC switch, a flexible AC / DC switch, a DC new energy source, and an AC new energy source. Figure 2 The distribution system of the flexible interconnected distribution network provided is connected through a three-port AC / DC flexible interconnection switch to achieve continuous control of the power of the distribution network and flexible control of operation, meeting customized power needs such as distributed power generation, high power supply reliability and high-quality power supply. Figure 2 It is only a specific example of a specific flexible interconnected distribution network targeted by this application, and of course it can also be used in Figure 2 The flexible interconnected distribution network shown has other features that do not conflict with the solutions provided in this application.

[0078] Figure 3 and Figure 4Two specific structural schematic diagrams of voltage source converters used in the present application are also shown, wherein a two-level VSC has an IGBT, each IGBT has a reverse diode connected in parallel therewith, and each valve includes a plurality of IGBT / diode components connected in series. Pulse width modulation (PWM) is used to control the IGBT to help form the waveform, because the IGBT is turned on and off multiple times when implementing PWM, switching losses will occur, and harmonics are a factor; the three-level VSC further improves the harmonic problem, and the three-level VSC has four IGBT valves per phase, of which two diode valves (which can be replaced by IGBTs for better controllability) are used to clamp the voltage, and the top two IGBTs are opened to obtain a higher voltage level, the middle two IGBTs are opened to obtain an intermediate (or zero) voltage level, and the bottom two valves are opened to obtain a lower voltage level.

[0079] The optimization scheduling method for the flexible interconnected distribution network provided in the present application constructs a voltage source converter small disturbance stability slope optimization model, and uses the voltage source converter small disturbance stability slope optimization model to reasonably and accurately schedule the various components of the flexible interconnected distribution network, thereby optimizing the grid operation efficiency on the basis of achieving effective scheduling of the power system while ensuring system stability. This scheme takes into account multiple scenarios and multiple constraints, and is more suitable for complex and changeable modern power systems.

[0080] This embodiment adopts Figure 5 A flowchart of a method for obtaining a small disturbance stability constraint with an added input is provided, which specifically comprises the following steps:

[0081] Step 301: pre-define various possible normal operation scenarios, including different load levels and renewable energy output conditions, each of which represents a specific grid operation condition, including: high load, low load, high solar energy output or low wind energy output;

[0082] Step 302: Perform power system flow calculation for each normal operation scenario, where the power system flow calculation includes calculation of various parameters including voltage level, line power flow, and system loss;

[0083] Step 303: Perform stability analysis on the normal operation scenario completed by the power system flow calculation to obtain the expected scenario and the extreme source-load scenario that meet the stability requirements;

[0084] Step 304: taking the constraints of each desired scenario and each extreme source-load scenario as initial small-disturbance stability constraints;

[0085] An implementation method including but not limited to may include the following steps:

[0086] Firstly, the eigenvalues ​​of each normal operation scenario completed by the power system flow calculation are calculated respectively, and when the real part of the eigenvalue is negative, it is determined that the corresponding normal operation scenario meets the stability condition, and the expected scenario and the extreme source and load scenario are obtained;

[0087] The constraints of the expected scenario and the extreme source-load scenario are used as initial small-disturbance stability constraints.

[0088] Step 305: Apply matrix perturbation theory to linearize the initial small disturbance stability constraint to obtain the linearized small disturbance stability constraint that can be added to the initial model.

[0089] An implementation method including but not limited to may include the following steps:

[0090] Firstly, the calculation process of the initial small disturbance stability constraint is simplified by using matrix perturbation theory to obtain the simplified constraint. Matrix perturbation theory is used to analyze the change of the eigenvalue of the state matrix when the parameters of the power grid system change slightly.

[0091] Among them, considering that the constraint is essentially a limiting condition, and whether the limiting condition is met needs to be calculated, and the characteristics of the matrix perturbation theory are combined to analyze the changes in the eigenvalues ​​of the state matrix when the parameters of the power grid system change slightly, that is, the purpose of the simplified operation in this step is to simplify the conditions of whether the small disturbance stability constraint is met when the eigenvalue changes slightly, that is, to reduce the amount of calculation and sensitivity.

[0092] Convert the simplified constraints into linear or quasi-linear forms to obtain converted constraints;

[0093] Optimization algorithms including interior point method and gradient method are selected to optimize the post-conversion constraints to obtain the final small disturbance stability constraints that can be actually added to the initial model. The optimization algorithm is adapted to the model characteristics of the small disturbance stability slope optimization model of the voltage source converter.

[0094] This embodiment provides an implementation method for constructing initial constraints and linearizing the initial constraints based on the following situations of the expected scenario through steps 301 to 305, that is, considering various possible scenarios under normal operating conditions, such as different load levels, renewable energy output, etc., to ensure that the system remains stable under these conditions.

[0095] First, define a series of possible normal operation scenarios, including different load levels, renewable energy output, etc., and each scenario should represent a specific grid operating condition, such as high load, low load, high solar output, low wind output, etc. Then, perform power system flow calculation for each scenario, which can be solved using classical algorithms such as the Newton-Raphson method. The flow calculation should include the calculation of parameters such as voltage level, line power flow, and system loss. Next, perform system stability analysis for each scenario to ensure that the system parameters meet stability requirements in all scenarios. This may include calculating the eigenvalues ​​in each scenario to ensure that they meet stability conditions (for example, the real part of the eigenvalue should be negative). Finally, integrate the constraints of all scenarios into the voltage source converter small disturbance stability slope optimization model, and ensure that the stability constraints of all scenarios are met during the optimization process.

[0096] This embodiment also passes Figure 6 The figure shows a specific power flow calculation scheme from the front, including the following steps:

[0097] Step 401: Initialize the system: determine the load and power generation conditions of the system, including the active and reactive loads of each node, and the active and reactive outputs of the generator; specify initial voltage estimates for all nodes;

[0098] Determine the load and generation conditions: First, determine the load and generation conditions of each node in the power system, including active and reactive loads, and the active and reactive output of the generator. For load nodes, record their active and reactive power requirements; for generator nodes, record their active and reactive output capabilities.

[0099] Specify initial voltage estimates: Specify the estimated values ​​of the initial voltage magnitude and phase angle for all nodes. In general, the initial voltage magnitude of all load nodes is set to 1.0 pu (per unit), and the phase angle is usually set to 0°, while the initial voltage magnitude of the generator node is determined by the system's generator setting value, and the phase angle is set according to the system's initial state.

[0100] Step 402: Calculate power imbalance: For each node, calculate a power imbalance amount, where the power imbalance amount refers to the difference between the actual injected power and the expected injected power;

[0101] The power imbalance of each node can be expressed as: Ploss = Pin-Pout, Qloss = Qin-Qout, where Pin and Qin are the actual active and reactive power injected into the node, and Pout and Qout are the expected power of the node (from the initial estimate of the power flow calculation).

[0102] Step 403: constructing a Jacobian matrix for approximating the nonlinear behavior of the system, where the Jacobian matrix is ​​a partial derivative matrix of the nonlinear equations of the system;

[0103] In the Newton-Raphson method, the Jacobian matrix is ​​used to describe the nonlinear behavior of the system. This matrix consists of the partial derivatives of the system's power equation with respect to the voltage magnitude and phase angle. The Jacobian matrix contains two parts: one is the derivative with respect to the voltage magnitude, and the other is the derivative with respect to the voltage phase angle. For each node, the elements of the Jacobian matrix can be calculated based on the power equation of the node, which usually includes the active power equation and the reactive power equation.

[0104] Step 404: Solve the linear equations: Use Newton-Raphson iteration method to solve the linear equations to update the voltage magnitude and phase angle;

[0105] According to the Newton-Raphson method, the constructed linear equations can be expressed as: ΔV = -J -1 ΔP, where ΔV is the correction value of voltage amplitude and phase angle, J -1 is the inverse of the Jacobian matrix, and ΔP is the vector of power imbalances (the difference between active and reactive power).

[0106] Step 405: Update node voltage: Use the obtained solution to update the voltage amplitude and phase angle of each node, and recalculate the injected power of each node;

[0107] The voltage magnitude and phase angle of each node are updated using the iteratively solved correction values. These updated voltage values ​​will be used to calculate the power imbalance in the next calculation. Specifically, the new voltage magnitude and phase angle Will be updated in the following ways:

[0108]

[0109] Where, ΔV i and Δθ i are the correction values ​​of voltage amplitude and phase angle respectively.

[0110] After the update is completed, the power imbalance usually needs to be calculated again, that is, the power imbalance of each node is recalculated using the updated voltage amplitude and phase angle. These new power imbalances will be used as input for the next iteration to update the power balance state of the system.

[0111] Step 406: Convergence judgment: Check whether the power imbalance is less than the predetermined tolerance limit. If so, it is determined that the convergence and power flow calculation are completed; otherwise, the power imbalance calculation step is repeated to continue the iteration.

[0112] That is, check whether the power imbalance is less than the predetermined tolerance limit (for example, the sum of the squares of the power imbalance is less than a small threshold). If so, the power flow calculation is considered to have converged and the calculation can be terminated. If the power imbalance is still large, it is necessary to continue iteration.

[0113] Convergence condition: For each node, calculate the absolute error of its active and reactive power imbalance, and determine whether the errors of all nodes meet the set tolerance limit. If the convergence condition is met, the calculation can be terminated and the final voltage, power and phase angle results can be output. If the convergence condition is not met, return to step 404 and continue to iterate.

[0114] Furthermore, when performing linearization processing, this embodiment preferably uses matrix perturbation theory to simplify the calculation of small disturbance stability constraints, which can help analyze the changes in the eigenvalues ​​of the state matrix under small changes in system parameters; linearization constraints are to linearize small disturbance stability constraints to simplify the calculation process, which involves converting nonlinear stability constraints into linear or quasi-linear forms, making the model easier to solve; algorithm selection, select a suitable algorithm to solve the optimization problem after linearization. Considering the characteristics of the model, it may be necessary to adopt a special optimization algorithm, such as the interior point method, the gradient method, etc.

[0115] Furthermore, for step 102, this embodiment also provides a more specific implementation method:

[0116] Upper-level algorithm: Genetic algorithm is used to randomly generate the initial population, including the following steps:

[0117] Initialize the population and randomly generate the initial population. Each individual represents a potential solution, usually a parameter vector;

[0118] Fitness evaluation, the fitness of each individual is evaluated. The fitness function is defined according to the goal of the optimization problem, such as minimizing cost or loss;

[0119] Genetic operations, applying selection, crossover and mutation operations to generate a new generation of population; the selection operation is based on fitness, selecting individuals with higher fitness to enter the next generation; the crossover operation generates offspring by combining the characteristics of parent individuals; the mutation operation introduces new genetic variation by randomly changing some characteristics of individuals;

[0120] Iterative evolution repeats fitness evaluation and genetic operations until a predetermined number of iterations is reached or a stopping condition is met.

[0121] Lower-level algorithm: The sequential interior point method iteratively optimizes individuals within the population, including the following steps:

[0122] Local optimization: For each individual selected by the genetic algorithm, the sequence interior point method is used for local optimization;

[0123] Build a local model and construct a local optimization problem near each individual. This involves adding small perturbations based on the current point;

[0124] Solve local problems and use interior point methods to solve local optimization problems. This may include constructing Lagrangian functions and solving KKT (Karush-Kuhn-Tucker) conditions;

[0125] Update individuals: Update individuals in the genetic algorithm according to the results of local optimization.

[0126] Furthermore, the lower-level algorithm specifically includes the following steps:

[0127] Step 1: Local Optimization

[0128] Select an individual from the population generated by the genetic algorithm. This individual represents a potential solution to the optimization problem, usually in the form of a parameter vector.

[0129] Step 2: Build a local model

[0130] Local model construction, based on the selected individuals, constructs a local optimization problem by adding small perturbations, which can be expressed as small adjustments to the parameter vector.

[0131] Step 3: Solve the local problem

[0132] Construct the Lagrangian function. For optimization problems with constraints, construct the Lagrangian function L(x,λ)=f(x)+λTg(x), where: f(x) is the objective function, g(x) is the constraint function (usually an equality constraint), and λ is the Lagrangian multiplier.

[0133] Solve the KKT conditions. Solve the KKT conditions, that is, solve the following system of equations:

[0134] here is the gradient of the Lagrangian function with respect to x, and g(x) = 0 represents the constraint condition.

[0135] Application of Interior Point Methods,In interior point methods, constraints are usually handled by introducing a barrier function, which keeps the optimization path inside the feasible region.,For each iteration, x and λ are updated, usually by solving a system of linear or nonlinear equations.

[0136] Step 4: Update individual

[0137] Update parameters and update individuals in the genetic algorithm according to the solution results; the updated individuals reflect the results of local optimization.

[0138] Through the above steps, the sequential interior point method carefully optimizes each selected individual in a local range, thereby improving the accuracy and efficiency of the overall optimization process. This method is particularly suitable for complex optimization problems, such as the VSC small disturbance stability slope optimization problem in power systems, which requires considering multiple constraints and objective functions at the same time.

[0139] Based on any of the above embodiments, step 103 may specifically include the following steps:

[0140] 1. Strategy input

[0141] Vertical reference point and source load power expectation scenario:

[0142] Define a series of source and load (i.e. power source and load) power scenarios that reflect the expected operating status of the power grid in different time intervals; vertical reference points usually refer to voltage reference points, which are crucial in power system stability analysis.

[0143] 2. Goal Setting

[0144] Minimizing total network loss: Define the objective function, such as minimizing the total network loss. The formula may be: min∑(Ploss), where Ploss represents the network loss in each scenario.

[0145] 3. Optimize VSC droop slope

[0146] Droop control model, VSC droop control can be expressed by the following formula: P = P0 + kp (Vref-V) and Q = Q0 + kq (Vref-V). Here P0 and Q0 are the reference active and reactive outputs of the VSC, kp and kq are the slope parameters of the droop control, Vref is the reference voltage, V is the actual voltage, and the optimization goal is to determine the best kp and kq to minimize the total network loss.

[0147] 4. Constraints

[0148] The DC distribution network flow constraint ensures that the power flow in all scenarios meets the physical and operational safety constraints. The formula can be expressed as: Pgen-Pload=Ploss, where Pgen and Pload are the power generation and load respectively.

[0149] VSC power constraint: The power output of the VSC should comply with its capacity constraints, such as: Pmin≤P≤Pmax and Qmin≤Q≤Qmax.

[0150] Small disturbance stability constraint: Ensure the small disturbance stability of the system in all scenarios, usually achieved by analyzing the system eigenvalues.

[0151] Because the situation is complicated, it is impossible to list them one by one for explanation. Those skilled in the art should be aware that there may be many examples based on the basic method principles provided by this application combined with actual conditions, and all of them should be within the scope of protection of this application without sufficient creative work.

[0152] See below Figure 7 , Figure 7 This is a structural block diagram of an optimization dispatching system 500 for a flexible interconnected distribution network provided in an embodiment of the present application. This embodiment exists as a system embodiment corresponding to the above method embodiment. The optimization dispatching system 500 for a flexible interconnected distribution network may include:

[0153] The first unit 501 is used to establish a voltage source converter small disturbance stability slope optimization model by taking a preset index as a target of an initial model and adding a small disturbance stability constraint after linearization processing by applying matrix perturbation theory in an expected scenario and an extreme source-load scenario to the initial model;

[0154] The second unit 502 is used to optimize the small disturbance stability slope optimization model of the voltage source converter by combining the sequential interior point method and the genetic algorithm to obtain an optimized model;

[0155] The third unit 503 is used to consider the expected scenario set of source-load power within the vertical reference point and the scheduling interval as the scheduling strategy input, minimize the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimize the droop slope of the voltage source converter according to the target and input conditions, and use the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions to determine the actual scheduling strategy and actual constraints of the optimized model, and obtain the target optimization scheduling model;

[0156] The fourth unit 504 is used to schedule the components of the flexible interconnected distribution network using the target optimization scheduling model.

[0157] In some other implementations of this embodiment, the preset indicator also includes:

[0158] And target setting: The distance from the real part of the dominant eigenvalue to the imaginary axis is defined as the indicator of the small disturbance stability margin of the distribution network. The indicator is used to reflect the response capability of the power grid system to which the flexible interconnected distribution network belongs to small disturbances, and the speed at which the system recovers to a stable state;

[0159] The second indicator corresponding to the eigenvalue analysis: calculating the eigenvalue of the small signal state matrix of the power grid system to which the flexible interconnected distribution network belongs, and the eigenvalue represents the dynamic response of the state of the system;

[0160] The third indicator corresponding to the stability assessment: the stability of the power grid system is assessed according to the size and position of the real part of the dominant eigenvalue. The closer the real part of the dominant eigenvalue is to the imaginary axis, the higher the stability of the power grid system.

[0161] In some other implementations of this embodiment, the linearized small disturbance stability constraint added to the initial model is determined by the following steps:

[0162] Predefine various possible normal operation scenarios, including different load levels and renewable energy output conditions. Each normal operation scenario represents a specific grid operation condition, including high load, low load, high solar output or low wind output.

[0163] Perform power flow calculations for each normal operation scenario, including calculations of voltage levels, line power flows, and system losses;

[0164] Conduct stability analysis on the normal operation scenarios completed by power system flow calculation to obtain the expected scenarios and extreme source and load scenario target scenarios that meet stability requirements;

[0165] The constraints of each desired scenario and each extreme source-load scenario target scenario are used as the initial small-disturbance stability constraints;

[0166] The matrix perturbation theory is applied to linearize the initial small disturbance stability constraints, and the linearized small disturbance stability constraints are obtained which can be added to the initial model.

[0167] In some other implementations of this embodiment, the power system flow calculation is performed for each normal operation scenario, including:

[0168] Initializing the system: determining the load and power generation conditions of the system, including the active and reactive loads of each node, and the active and reactive outputs of the generator; specifying initial voltage estimates for all nodes;

[0169] Calculating power imbalance: For each node, calculating a power imbalance amount, wherein the power imbalance amount refers to the difference between the actual injected power and the expected injected power;

[0170] Constructing a Jacobian matrix used in the Newton-Raphson iteration method to describe the nonlinear behavior of the system, wherein the Jacobian matrix is ​​a partial derivative matrix of the nonlinear equations of the system;

[0171] The power imbalance and the Jacobian matrix are used to construct a linear equation system according to the Newton-Raphson iteration method: ΔV = -J -1 ΔP, where ΔV is the correction value of voltage amplitude and phase angle, J -1is the inverse matrix of the Jacobian matrix, ΔP is the vector of power imbalances;

[0172] Update node voltages: Solve the linear equations and use the solution to update the voltage magnitude at each node and phase angle for: Where, ΔV i and Δθ i are the correction values ​​of voltage amplitude and phase angle respectively; the power imbalance value of each node is recalculated using the updated voltage amplitude and phase angle; the recalculated power imbalance value is used as the input of the next iteration;

[0173] Convergence judgment: Check whether the power imbalance is less than a predetermined tolerance limit. If so, it is determined that convergence and power flow calculation are completed; otherwise, the calculation steps of solving the linear equations are repeated to continue iteration.

[0174] In some other implementations of this embodiment, stability analysis is performed on the normal operation scenario completed by the power system flow calculation to obtain a target scenario that meets the stability requirements, including:

[0175] The characteristic value of each normal operation scenario completed by the power system flow calculation is calculated respectively, and when the real part of the characteristic value is negative, it is determined that the corresponding normal operation scenario meets the stability condition, and the target scenario is obtained;

[0176] Correspondingly, the constraints of each target scene are taken as small disturbance stability constraints, including:

[0177] The constraints of all target scenarios are integrated into the voltage source converter small disturbance stability slope optimization model as small disturbance stability constraints, and it is ensured that the stability constraints of all scenarios are met during the optimization process.

[0178] In some other implementations of this embodiment, the matrix perturbation theory is applied to linearize the initial small disturbance stability constraint, including:

[0179] The matrix perturbation theory is used to simplify the calculation process of the initial small disturbance stability constraints and obtain the simplified constraints. The matrix perturbation theory is used to analyze the changes in the eigenvalues ​​of the state matrix when the parameters of the power grid system change slightly.

[0180] The simplified constraints of small disturbance stability are converted into linear or quasi-linear forms to simplify the calculation process, and the converted constraints are obtained;

[0181] Optimization algorithms including interior point method and gradient method are selected to optimize the post-conversion constraints to obtain the final small disturbance stability constraints that can be actually added to the initial model. The optimization algorithm is adapted to the model characteristics of the small disturbance stability slope optimization model of the voltage source converter.

[0182] In some other implementations of this embodiment, the strategy input is a vertical reference point and an expected load source power scenario. The expected load source power scenario is a predefined series of power scenarios of power sources and loads, which is used to reflect the expected operating status of the power grid in different time intervals. The vertical reference point is a voltage reference point used for calculations in power system stability analysis.

[0183] This embodiment exists as a system embodiment corresponding to the above method embodiment. Compared with the prior art, the optimization dispatching system of the flexible interconnected distribution network provided in this embodiment constructs a voltage source converter small disturbance stability slope optimization model, and uses the voltage source converter small disturbance stability slope optimization model to reasonably and accurately dispatch the various components of the flexible interconnected distribution network, thereby optimizing the grid operation efficiency on the basis of realizing effective dispatching of the power system, while ensuring system stability. This solution takes into account multiple scenarios and multiple constraints, and is more suitable for complex and changeable modern power systems.

[0184] Based on the above embodiments, the present application further provides an electronic device, which may include a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps provided in the above embodiments may be implemented. Of course, the electronic device may also include various necessary network interfaces, power supplies, and other components.

[0185] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by an execution terminal or a processor, the steps provided in the above embodiment can be implemented. The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0186] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0187] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0188] Specific examples are used herein to illustrate the principles and implementation methods of the present application, and the description of the above embodiments is only used to help understand the method and core ideas of the present application. For ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0189] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

Claims

1. A method for optimizing the dispatching of a flexible interconnected distribution network, characterized in that: include: A voltage source converter small disturbance stability slope optimization model is established by taking a preset index as a target of an initial model and adding a linearized small disturbance stability constraint to the initial model; wherein the preset index includes a first index of a distribution network small disturbance stability margin represented by a distance from a real part of a dominant eigenvalue to an imaginary axis; The voltage source converter small disturbance stability slope optimization model is optimized by combining a sequential interior point method with a genetic algorithm to obtain an optimized model; Taking into account the vertical reference point and the expected source-load power scenario set within the dispatching interval as the dispatching strategy input, minimizing the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimizing the droop slope of the voltage source converter according to the target and input conditions, taking the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions, determining the actual dispatching strategy and actual constraints of the optimized model, and obtaining the target optimization dispatching model; The target optimization scheduling model is used to schedule the various components of the flexible interconnected distribution network.

2. The method according to claim 1, characterized in that The preset indicators also include: A second indicator corresponding to the eigenvalue: calculating the eigenvalue of a small signal state matrix of the power grid system to which the flexible interconnected distribution network belongs, the eigenvalue representing a dynamic response of the state of the system; And a third indicator corresponding to stability: the stability of the power grid system is evaluated according to the size and position of the real part of the dominant eigenvalue, and the closer the real part of the dominant eigenvalue is to the imaginary axis, the higher the stability of the power grid system is.

3. The method according to claim 1, characterized in that The small disturbance stability constraint after linearization is determined by the following steps: Predefined normal operation scenarios; Performing power system flow calculation for each of the normal operating scenarios; Performing stability analysis on the normal operation scenario completed by the power system power flow calculation to obtain a target scenario that meets the stability requirements; Taking the constraint conditions of each target scene as initial small disturbance stability constraints; The initial small disturbance stability constraint is linearized by applying matrix perturbation theory to obtain a linearized small disturbance stability constraint that can be added to the initial model.

4. The method according to claim 3, characterized in that The performing power system flow calculation for each of the normal operating scenarios includes: Initializing the system: determining the load and power generation conditions of the system, including the active and reactive loads of each node, and the active and reactive outputs of the generator; specifying initial voltage estimates for all nodes; Calculating power imbalance: For each node, calculating a power imbalance amount, wherein the power imbalance amount refers to the difference between the actual injected power and the expected injected power; Constructing a Jacobian matrix used in the Newton-Raphson iteration method to describe the nonlinear behavior of the system, wherein the Jacobian matrix is ​​a partial derivative matrix of the nonlinear equations of the system; The power imbalance and the Jacobian matrix are used to construct a linear equation system according to the Newton-Raphson iteration method: ΔV = -J -1 ΔP, where ΔV is the correction value of voltage amplitude and phase angle, J -1 is the inverse matrix of the Jacobian matrix, ΔP is the vector of power imbalances; Update node voltages: Solve the linear equations and use the solution to update the voltage magnitude at each node and phase angle for: Where, ΔV i and Δθ i are the correction values ​​of voltage amplitude and phase angle respectively; the power imbalance value of each node is recalculated using the updated voltage amplitude and phase angle; the recalculated power imbalance value is used as the input of the next iteration; Convergence judgment: Check whether the power imbalance is less than a predetermined tolerance limit. If so, it is determined that convergence and power flow calculation are completed; otherwise, the calculation steps of solving the linear equations are repeated to continue iteration.

5. The method according to claim 3, characterized in that: The stability analysis of the normal operation scenario completed by the power system flow calculation to obtain a target scenario that meets the stability requirements includes: The characteristic value of each normal operating scenario completed by the power system flow calculation is calculated respectively, and when the real part of the characteristic value is negative, it is determined that the corresponding normal operating scenario meets the stability condition, so as to obtain the target scenario.

6. The method according to claim 4 or 5, characterized in that: The matrix perturbation theory is used to linearize the initial small disturbance stability constraints, including: The calculation process of the initial small disturbance stability constraint is simplified by using the matrix perturbation theory to obtain a simplified constraint, wherein the matrix perturbation theory is used to analyze the change of the eigenvalue of the state matrix when the parameters of the power grid system change slightly; Converting the simplified constraint into a linear or quasi-linear form to obtain a converted constraint; An optimization algorithm including an interior point method and a gradient method is selected to optimize the post-conversion constraints to obtain a final small disturbance stability constraint that can be actually added to the initial model. The optimization algorithm is adapted to the model characteristics of the voltage source converter small disturbance stability slope optimization model.

7. The method according to claim 6, characterized in that The voltage source converter small disturbance stability slope optimization model is optimized by combining the sequential interior point method and the genetic algorithm to obtain an optimized model, which specifically includes: The upper layer uses the genetic algorithm to randomly generate an initial population, and the lower layer uses the sequence interior point method to iteratively optimize the individuals in the population, and adds a characteristic value verification step in the solution process of each iterative optimization.

8. An optimization dispatching system for a flexible interconnected distribution network, characterized in that: include: The first unit is used to establish a small disturbance stability slope optimization model of a voltage source converter by taking a preset index as a target of an initial model and adding a small disturbance stability constraint after linearization processing by applying matrix perturbation theory under an expected scenario and an extreme source-load scenario to the initial model; The second unit is used to optimize the voltage source converter small disturbance stability slope optimization model by combining the sequential interior point method and the genetic algorithm to obtain an optimized model; The third unit is used to consider the vertical reference point and the source-load power expected scenario set within the scheduling interval as the scheduling strategy input, minimize the total network loss of multiple expected scenarios as the optimization setting target of the optimized model, optimize the voltage source converter droop slope according to the target and input conditions, and use the DC distribution network power constraint, the voltage source converter power constraint and the small disturbance stability constraint after linearization as constraint conditions to determine the actual scheduling strategy and actual constraints of the optimized model to obtain the target optimization scheduling model; The fourth unit is used to dispatch the components of the flexible interconnected distribution network using the target optimization scheduling model.

9. An electronic device, characterized in that: include: Memory, for computer programs; A processor, configured to implement the steps of the method for optimizing scheduling of a flexible interconnected distribution network as described in any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and after being executed by a processor, the computer program can implement each step of the method for optimizing scheduling of a flexible interconnected distribution network as described in any one of claims 1 to 7.

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