An igdt-based power system risk assessment and reactive power optimization method and system

Abstract

This invention presents a power system risk assessment and reactive power optimization method and system based on Inductively Coupled Logic Optimization (IGDT). It establishes a deterministic reactive power optimization model with the objective of minimizing the linear weighted average of system network losses, voltage deviations, and operating costs, and solves this model to obtain a baseline optimal solution. For the uncertainty of renewable energy output and load power, IGDT is used for modeling, constructing a robust optimization model with the objective of maximizing the range of uncertainty fluctuations. Finally, the optimal solution is obtained by iteratively solving the IGDT robust optimization model using a bisection method. This invention effectively quantifies the voltage exceedance risk immunity level and system performance, providing dispatchers with clear and quantitative decision-making basis. It eliminates the need to rely on the probability distribution of uncertain variables, significantly reducing computational complexity. Simultaneously, it considers system economy and security, improves the robustness and flexibility of grid operation under high-proportion renewable energy integration, provides an optimal dispatch strategy to minimize risks, and achieves optimized operation under controllable risks.

Description

Technical Field

[0001] This invention belongs to the field of power system and reactive power optimization technology, and particularly relates to a power system risk assessment and reactive power optimization method and system based on IGDT. Background Technology

[0002] (1) In recent years, the penetration rate of intermittent renewable energy sources such as wind power and photovoltaics in the power grid has been increasing. The randomness and volatility of their output have brought great challenges to the safe and stable operation of the power system. Reactive power balance is an important prerequisite for ensuring system voltage quality, reducing network losses, and ensuring safe and stable operation.

[0003] Traditional deterministic reactive power optimization models cannot handle the uncertainties of renewable energy sources and loads, and the optimization results may be overly optimistic, easily leading to problems such as voltage exceeding limits in actual operation. Currently, the main methods for handling uncertainties in optimization problems are stochastic optimization and robust optimization. Stochastic optimization requires the precise probability distribution function of the uncertain variables, which is often difficult to obtain accurately in practice. Traditional robust optimization aims to ensure system operation under the worst-case scenario, and the results are usually too conservative and economically inefficient; neither can effectively address the reactive power optimization problem of renewable energy power systems with high penetration rates.

[0004] (2) Information gap decision theory (IGDT) is a decision-making method that can handle deep uncertainty without the need for probabilistic models. By describing the fluctuation range of uncertainty parameters, IGDT constructs robust optimization models or opportunity-seeking models, providing decision-makers with a quantitative tool for balancing "risk" and "reward". Applying IGDT to voltage over-limit risk quantification and reactive power optimization can effectively overcome the shortcomings of traditional stochastic optimization models, such as strong dependence on probabilistic models and complex calculations of scenario-based methods, providing a brand-new decision-making tool for the safe, economical, and robust operation of power systems under the background of "high voltage and high efficiency". Summary of the Invention

[0005] Compared to traditional methods, this approach effectively addresses the technical challenges of existing reactive power optimization models in source-load uncertainty environments. These models are either impractical due to their reliance on precise probability distributions of uncertain parameters, or overly conservative and economical. Furthermore, they cannot quantitatively assess voltage exceedance risks, making it difficult to achieve a flexible trade-off between safety and economy. Specifically, this method achieves the following breakthroughs by introducing Information Gap Decision Theory (IGDT): 1) It allows modeling based solely on the fluctuation range of uncertain parameters, eliminating the need for probabilistic information and overcoming the shortcomings of stochastic programming, which relies heavily on precise probability distributions and struggles to obtain actual data; 2) It quantifies voltage exceedance risks as a manageable uncertainty margin for the system, providing a clear physical indicator that intuitively characterizes the system's ability to cope with power system risks, thus offering reliable decision-making support for operators.

[0006] The present invention adopts the following technical solution.

[0007] This invention proposes a power system risk assessment and reactive power optimization method based on IGDT, comprising:

[0008] A deterministic reactive power optimization model is established based on the power system. The objective function of this model is the minimum linearly weighted sum of total system active power loss, total system voltage deviation, and total operating cost of reactive power compensation equipment, with corresponding constraints. The total operating cost of the reactive power compensation equipment refers to the operating cost of the on-load tap-changing transformer (OLTC). Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The summation is based on the corresponding unit capacity operating cost weight parameters, wherein the unit capacity operating cost weight parameters are updated each time the IGDT robust optimization model is iterated and solved;

[0009] Identify sources of uncertainty in the power system, construct an uncertainty set, and set corresponding constraints;

[0010] Based on the deterministic reactive power optimization model and the uncertainty set, an IGDT robust optimization model is constructed; the objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set.

[0011] The optimal robust decision is obtained by iteratively solving the IGDT robust optimization model using a bisection method.

[0012] The optimal robust decision is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

[0013] More preferably, the operating cost of the on-load tap-changing transformer (OLTC) is... Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The specific calculation method is as follows:

[0014] The unit capacity cost weighting parameter of each OLTC device is multiplied by the absolute value of the equivalent voltage regulation corresponding to the current OLTC level to obtain the third multiplication result. The third multiplication results of all OLTC devices are then added together to obtain the final result. The equivalent voltage regulation is obtained by multiplying the rated voltage change corresponding to each OLTC level by the current level, and then normalized by the system voltage sensitivity matrix with respect to reactive power.

[0015] The unit capacity operating cost weight parameter of each CB device is multiplied by the absolute value of the reactive power compensated by the CB at the current moment to obtain the fourth multiplication result. The fourth multiplication results of all CB devices are then added together to obtain the result. ;

[0016] The fifth multiplication result is obtained by multiplying the unit capacity operating cost weight parameter of each UPFC device by the absolute value of the reactive power output setpoint of the UPFC device at the current moment. The fifth multiplication results of all UPFC devices are then added together to obtain the final result. .

[0017] More preferably, the calculation process for the unit capacity cost weighting parameter is as follows:

[0018] Control variables are selected for OLTC equipment, CB equipment and UPFC equipment respectively to form a control variable set U;

[0019] Calculate the normalized trajectory sensitivity index for each device based on the set of control variables U. ;

[0020] The normalized trajectory sensitivity index This is converted into the unit capacity cost weighting parameter.

[0021] More preferably, power flow calculations are performed based on current real-time power grid data to obtain a reference operating point. The voltage of each node below ;

[0022] For each device k in the set of control variables U, at the reference value of device k Apply a small perturbation to it. Meanwhile, all other control variables remain unchanged;

[0023] For each disturbance scenario of each device k, perform power flow calculation again to obtain the node voltage after the disturbance. ;

[0024] Subtract the node voltage of device k after disturbance from the voltage of device k at the reference operating point. The node voltage below The result of the subtraction is divided by the amount of small perturbation applied to device k. To obtain the control variables of device k Trajectory sensitivity to node voltage V;

[0025] Each of the control variables The ratio between the corresponding reference value and the system voltage reference value is calculated, and the trajectory sensitivity is multiplied by the ratio to obtain the normalized trajectory sensitivity index for each device k. .

[0026] More preferably, the normalized trajectory sensitivity index is... The steps for converting the unit capacity cost weighting parameter include:

[0027] Based on the normalized comprehensive trajectory sensitivity index Calculate the relative efficiency coefficient of each reactive power compensation device; the relative efficiency coefficient is the coefficient of a certain device. With the largest of all devices The ratio of .

[0028] For each piece of equipment, the original static cost weight parameter of the equipment is multiplied by an adjustment factor determined by the balance factor and the relative efficiency coefficient to obtain the unit capacity cost weight parameter of the equipment.

[0029] The adjustment factor is inversely proportional to the relative efficiency coefficient, and the balance factor is used to set the trade-off between static cost and dynamic efficiency.

[0030] More preferably, the specific steps for identifying uncertainty sources in the power system and constructing an uncertainty set include:

[0031] An uncertainty vector is defined based on the aforementioned uncertainty sources; the uncertainty vector is composed of the active / reactive power output of renewable energy sources and the active / reactive power of loads in the power system;

[0032] The uncertainty set contains all uncertainty vectors that satisfy the following condition: the absolute value of the relative deviation between the actual value and the corresponding predicted value of each uncertainty parameter in the set is not greater than a given fluctuation range parameter; the fluctuation range parameter is a non-negative decision variable.

[0033] The fluctuation range parameter is a non-negative variable used to quantify the magnitude of uncertainty, and the specific value of the non-negative variable is determined by solving the IGDT robust optimization model.

[0034] More preferably, the specific steps for iteratively solving the IGDT robust optimization model using the bisection method to obtain the comprehensive optimal solution are as follows:

[0035] The predicted values ​​of the uncertain variables in the uncertainty set are used as known inputs and substituted into the deterministic reactive power optimization model for solution, thereby obtaining the optimal solution of the system under the baseline scenario. ;

[0036] Set the deviation factor according to the actual situation. Based on the benchmark optimal solution The degree of fluctuation of the uncertain variable was calculated to be Expected solution at time ;

[0037] The degree of fluctuation of the uncertain variable is set as follows: upper bound of the search range and the lower realm And set the convergence precision Within the search interval, iterate through the following steps to find the maximum robust uncertainty fluctuation range:

[0038] The midpoint of the current search interval is taken as the trial fluctuation range for this iteration. And fix it; determine the uncertainty set based on the proposed fluctuation range; based on the uncertainty set, solve the deterministic reactive power optimization model that satisfies all constraints, and obtain the maximum possible cost under the worst uncertainty scenario. ;

[0039] Compare and The size relationship between the two, if ≤ Then update the lower bound of the search interval, making = Conversely, make The next iteration will be performed based on the newly generated search interval.

[0040] satisfy When the iteration stops, the maximum fluctuation parameter is output. and corresponding robust decision .

[0041] This invention also proposes a power system risk assessment and reactive power optimization system based on IGDT, including a deterministic reactive power optimization model construction module, an uncertainty set construction module, and an IGDT robust optimization model solving module:

[0042] A deterministic reactive power optimization model construction module is used to establish a deterministic reactive power optimization model based on the power system. The deterministic reactive power optimization model takes the minimum value of the linear weighted average of the total active power loss of the system, the total voltage deviation of the system, and the total operating cost of the reactive power compensation equipment as the objective function, and sets corresponding constraints.

[0043] The uncertainty set construction module identifies uncertainty sources in the power system, constructs uncertainty sets, and sets corresponding constraints.

[0044] The IGDT robust optimization model solution module constructs an IGDT robust optimization model based on the deterministic reactive power optimization model and the uncertainty set. The objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set. The IGDT robust optimization model is iteratively solved using the bisection method to obtain the comprehensive optimal solution. The comprehensive optimal solution is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

[0045] The present invention also proposes a terminal, including a processor and a storage medium:

[0046] The storage medium is used to store instructions;

[0047] The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0048] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] 1. This invention achieves intelligent self-adaptation of the optimization strategy by introducing a dynamic adjustment mechanism for the cost weight of reactive power compensation equipment based on trajectory sensitivity. This invention calculates the trajectory sensitivity of control variables of equipment such as OLTC, CB, and UPFC to system voltage in real time and dynamically adjusts their cost weights accordingly. This allows the optimization model to prioritize equipment with higher voltage regulation efficiency under the current grid conditions. This mechanism significantly improves the accuracy and economy of the optimization results, achieving a leap from "static empirical weights" to "dynamic intelligent weights," enabling the optimization strategy to adapt to different operating conditions.

[0051] 2. This invention constructs a multi-layered defense system that balances economy, security, and robustness by combining information gap decision theory with an adaptive dynamic weighting mechanism. This invention does not simply apply IGDT to a static model, but rather deeply couples it with the aforementioned dynamic weighting mechanism. In each iteration of the IGDT bisection method to solve the robust model, the dynamic cost weights of the devices are updated based on the current system state. This means that the final robust scheduling strategy is not only immune to uncertainty but also based on the optimal real-time cost-effectiveness of the devices. This combination effectively overcomes the drawback of traditional robust optimization results being overly conservative. While ensuring voltage safety (robustness), it maximizes the economic potential of the system, providing dispatchers with a better decision-making scheme under controllable risks.

[0052] 3. This invention provides a complete and universal reactive power optimization decision-making framework, whose core method is independent of specific grid structures or equipment types. The dynamic weighting mechanism proposed in this invention allows for the flexible definition of various control variables (such as OLTC taps, CB group number, UPFC reactive power output, photovoltaic inverter power factor, etc.), making it applicable to various modern grid scenarios containing traditional equipment, FACTS devices, and high proportions of distributed energy. Simultaneously, the IGDT model only requires the fluctuation range of uncertain parameters, without the need for precise probability distributions, reducing data requirements and computational complexity. This universality and practicality make the method of this invention easy to promote and apply in actual power grids, providing an effective tool for the safe, economical, and robust operation of "high-energy-consuming and high-polluting" power systems. Attached Figure Description

[0053] Figure 1 This is a flowchart of a power system risk assessment and reactive power optimization method based on IGDT according to the present invention;

[0054] Figure 2 This is a flowchart of the method according to Embodiment 1 of the present invention;

[0055] Figure 3 This is a simplified diagram of the uncertainty model of this invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0057] like Figure 1As shown, this invention proposes a power system risk assessment and reactive power optimization method based on IGDT, comprising:

[0058] A deterministic reactive power optimization model is established based on the power system. The objective function of this model is the minimum linearly weighted sum of total system active power loss, total system voltage deviation, and total operating cost of reactive power compensation equipment, with corresponding constraints. The total operating cost of the reactive power compensation equipment refers to the operating cost of the on-load tap-changing transformer (OLTC). Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The summation is based on the corresponding unit capacity operating cost weight parameters, wherein the unit capacity operating cost weight parameters are updated each time the IGDT robust optimization model is iterated and solved;

[0059] The operating cost of the on-load tap-changing transformer (OLTC) Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The specific calculation method is as follows:

[0060] The unit capacity cost weighting parameter of each OLTC device is multiplied by the absolute value of the equivalent voltage regulation corresponding to the current OLTC level to obtain the third multiplication result. The third multiplication results of all OLTC devices are then added together to obtain the final result. The equivalent voltage regulation is obtained by multiplying the rated voltage change corresponding to each OLTC level by the current level, and then normalized by the system voltage sensitivity matrix with respect to reactive power.

[0061] The unit capacity operating cost weight parameter of each CB device is multiplied by the absolute value of the reactive power compensated by the CB at the current moment to obtain the fourth multiplication result. The fourth multiplication results of all CB devices are then added together to obtain the result. ;

[0062] The fifth multiplication result is obtained by multiplying the unit capacity operating cost weight parameter of each UPFC device by the absolute value of the reactive power output setpoint of the UPFC device at the current moment. The fifth multiplication results of all UPFC devices are then added together to obtain the final result. .

[0063] The calculation process for the unit capacity cost weighting parameter is as follows:

[0064] Control variables are selected for OLTC equipment, CB equipment and UPFC equipment respectively to form a control variable set U;

[0065] Calculate the normalized trajectory sensitivity index for each device based on the set of control variables U. ;

[0066] The normalized trajectory sensitivity index This is converted into the unit capacity cost weighting parameter.

[0067] Based on the current real-time power grid data, power flow calculations are performed to obtain the reference operating point. The voltage of each node below ;

[0068] For each device k in the set of control variables U, at the reference value of device k Apply a small perturbation to it. Meanwhile, all other control variables remain unchanged;

[0069] For each disturbance scenario of each device k, perform power flow calculation again to obtain the node voltage after the disturbance. ;

[0070] Subtract the node voltage of device k after disturbance from the voltage of device k at the reference operating point. The node voltage below The result of the subtraction is divided by the amount of small perturbation applied to device k. To obtain the control variables of device k Trajectory sensitivity to node voltage V;

[0071] Each of the control variables The ratio between the corresponding reference value and the system voltage reference value is calculated, and the trajectory sensitivity is multiplied by the ratio to obtain the normalized trajectory sensitivity index for each device k. .

[0072] The normalized trajectory sensitivity index The steps for converting the unit capacity cost weighting parameter include:

[0073] Based on the normalized comprehensive trajectory sensitivity index Calculate the relative efficiency coefficient of each reactive power compensation device; the relative efficiency coefficient is the coefficient of a certain device. With the largest of all devices The ratio of .

[0074] For each piece of equipment, the original static cost weight parameter of the equipment is multiplied by an adjustment factor determined by the balance factor and the relative efficiency coefficient to obtain the unit capacity cost weight parameter of the equipment.

[0075] The adjustment factor is inversely proportional to the relative efficiency coefficient, and the balance factor is used to set the trade-off between static cost and dynamic efficiency.

[0076] Identify sources of uncertainty in the power system, construct an uncertainty set, and set corresponding constraints;

[0077] The specific steps for identifying uncertainty sources in the power system and constructing an uncertainty set include:

[0078] An uncertainty vector is defined based on the aforementioned uncertainty sources; the uncertainty vector is composed of the active / reactive power output of renewable energy sources and the active / reactive power of loads in the power system;

[0079] The uncertainty set contains all uncertainty vectors that satisfy the following condition: the absolute value of the relative deviation between the actual value and the corresponding predicted value of each uncertainty parameter in the set is not greater than a given fluctuation range parameter; the fluctuation range parameter is a non-negative decision variable.

[0080] The fluctuation range parameter is a non-negative variable used to quantify the magnitude of uncertainty, and the specific value of the non-negative variable is determined by solving the IGDT robust optimization model.

[0081] Based on the deterministic reactive power optimization model and the uncertainty set, an IGDT robust optimization model is constructed; the objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set.

[0082] The optimal robust decision is obtained by iteratively solving the IGDT robust optimization model using a bisection method.

[0083] The specific steps for iteratively solving the IGDT robust optimization model using the bisection method to obtain the comprehensive optimal solution are as follows:

[0084] The predicted values ​​of the uncertain variables in the uncertainty set are used as known inputs and substituted into the deterministic reactive power optimization model for solution, thereby obtaining the optimal solution of the system under the baseline scenario. ;

[0085] Set the deviation factor according to the actual situation. Based on the benchmark optimal solution The degree of fluctuation of the uncertain variable was calculated to be Expected solution at time ;

[0086] The degree of fluctuation of the uncertain variable is set as follows: upper bound of the search range and the lower realm And set the convergence precision Within the search interval, iterate through the following steps to find the maximum robust uncertainty fluctuation range:

[0087] The midpoint of the current search interval is taken as the trial fluctuation range for this iteration. And fix it; determine the uncertainty set based on the proposed fluctuation range; based on the uncertainty set, solve the deterministic reactive power optimization model that satisfies all constraints, and obtain the maximum possible cost under the worst uncertainty scenario. ;

[0088] Compare and The size relationship between the two, if ≤ Then update the lower bound of the search interval, making = Conversely, make The next iteration will be performed based on the newly generated search interval.

[0089] satisfy When the iteration stops, the maximum fluctuation parameter is output. and corresponding robust decision .

[0090] The optimal robust decision is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

[0091] This invention also proposes a power system risk assessment and reactive power optimization system based on IGDT, including a deterministic reactive power optimization model construction module, an uncertainty set construction module, and an IGDT robust optimization model solving module:

[0092] A deterministic reactive power optimization model construction module is used to establish a deterministic reactive power optimization model based on the power system. The deterministic reactive power optimization model takes the minimum value of the linear weighted average of the total active power loss of the system, the total voltage deviation of the system, and the total operating cost of the reactive power compensation equipment as the objective function, and sets corresponding constraints.

[0093] The uncertainty set construction module identifies uncertainty sources in the power system, constructs uncertainty sets, and sets corresponding constraints.

[0094] The IGDT robust optimization model solution module constructs an IGDT robust optimization model based on the deterministic reactive power optimization model and the uncertainty set. The objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set. The IGDT robust optimization model is iteratively solved using the bisection method to obtain the comprehensive optimal solution. The comprehensive optimal solution is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

[0095] The present invention also proposes a terminal, including a processor and a storage medium:

[0096] The storage medium is used to store instructions;

[0097] The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0098] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments.

[0099] Example 1

[0100] like Figure 2 As shown, this invention provides a power system risk assessment and reactive power optimization method based on IGDT / information gap decision theory.

[0101] The technical solution adopted in this invention is:

[0102] A deterministic reactive power optimization model is established, with the objective function being the minimum of the linear weighted average of the total active power loss, system voltage deviation, and total operating cost of reactive power compensation equipment. The comprehensive optimal solution under the baseline scenario is obtained by using the power flow equation and the safe operation limits of equipment and node voltages as constraints.

[0103] Identify the sources of uncertainty in the system (such as the active power output of renewable energy units, the active and reactive power of various loads, etc.) and model them using information gap decision theory (IGDT).

[0104] Based on the aforementioned deterministic and uncertain models, an IGDT robust optimization model is constructed, with the objective of maximizing the avoidance of the impact of uncertainty on the solution results.

[0105] The optimal robust decision is obtained by iteratively solving the constructed IGDT robust optimization model using the bisection method.

[0106] The system operators will use the optimal robust decision obtained from the solution as a reactive power scheduling scheme and issue it to each reactive power control device for execution.

[0107] The core mathematical expression of the deterministic reactive power optimization model in this method is:

[0108] 1) Objective function:

[0109] (1)

[0110] In the formula, (i=1,2,3) are the weight coefficients of each sub-objective function, and + + =1, the weighting coefficient is set based on engineering experience; It is the total active power loss of the system; It is a comprehensive measure of the voltage deviations at each node of the system; It is the total cost of reactive power compensation equipment, which is the linear sum of the operating costs of each compensation device. The compensation devices include OLTC, CB and UPFC.

[0111] The specific expressions for each sub-objective function are shown below:

[0112] Total active power loss of the system:

[0113] (2)

[0114] In the formula, The number of system branches; and Let t be the conductance and the phase angle difference of the node voltage between nodes i and j. and Let be the voltage amplitudes at nodes i and j at time t.

[0115] System voltage deviation:

[0116] (3)

[0117] In the formula, N is the number of nodes; This is the voltage rating of node i, typically taken as 1.0 pu.

[0118] Total cost of reactive power compensation equipment:

[0119] (4)

[0120] (5)

[0121] (6)

[0122] (7)

[0123] In the formula, The operating cost of on-load tap-changing transformers; Cost of reactive power compensation for capacitor banks; For UPFC reactive power compensation costs; This refers to the number of reactive power compensation devices; The unit capacity operating cost weight parameter for reactive power compensation device i can be obtained from trajectory sensitivity; It is the tap position of the on-load tap-changing transformer at time t; It is the voltage change corresponding to each tap of an on-load tap-changing transformer; It is the system voltage sensitivity matrix with respect to reactive power, which can be obtained by inverting the system Jacobian matrix. Let be the absolute value of the reactive power compensation capacity compensated by reactive power compensation device i at time t.

[0124] This invention introduces a real-time dynamic calculation mechanism for the weighting coefficients of reactive power compensation equipment based on trajectory sensitivity. The core purpose of this mechanism is to dynamically evaluate the voltage regulation efficiency of different reactive power compensation equipment according to the real-time operating status of the power grid, and accordingly correct the weighting parameters of the unit capacity operating cost of each equipment in the cost term of the objective function, i.e., formula (7). This allows the optimization model to not only consider the static operating costs of the equipment, but also to intelligently prioritize equipment with higher voltage regulation efficiency and greater contribution to improving system voltage deviation under the current conditions, thereby achieving the optimal trade-off between economy and regulation efficiency. The specific steps are as follows:

[0125] Step 1: Define the control variables for the reactive power compensation device. :

[0126] The dynamic weighting mechanism of this invention can be flexibly applied to various reactive power compensation devices. The definition of the control variable *u* is general, referring to a physical quantity that can be directly scheduled and significantly affect the reactive power flow of the system. In implementation, one or more combinations of the following variables can be selected as the control variable set based on the actual equipment configuration of the power grid. .

[0127] 1. Core control variables

[0128] For on-load tap-changing transformers (OLTC): Its tap position Or a variable ratio;

[0129] For capacitor banks / reactor banks (CB): The number of groups to be put into operation or the total reactive power compensation capacity determined therefrom. ;

[0130] For Unified Power Flow Controller (UPFC): The reactive power setpoint injected into it .

[0131] Furthermore, preferred alternative or additional control variables also include, for flexible AC transmission system equipment such as static synchronizing compensators: It can be defined as its reactive power output setpoint. For continuously adjustable shunt reactors or capacitors: It can be defined as its equivalent admittance value; for distributed resources such as distributed photovoltaic inverters: under the premise of allowing participation in reactive power regulation, It can be defined as its reactive power output setpoint. Or the power factor setting value; for generator excitation systems: It can be defined as its terminal voltage setting value. Or reactive power output setting value .

[0132] Based on core control variables In a typical implementation, U can be set to [OLTC tap position, number of CB connected groups, UPFC reactive power output setting]. Another preferred implementation could be set to [generator terminal voltage setting, distributed photovoltaic reactive power output setting, STATCOM reactive power output setting]. This flexibility ensures that the method of this invention can be applied to various scenarios, from traditional power grids to modern smart grids.

[0133] Step 2: Calculate the trajectory sensitivity of the equipment control variables to the system voltage.

[0134] This step aims to quantify the impact of a unit change in the control variable of each device on the system node voltage.

[0135] 1. Setting the reference operating point and disturbances: Based on the current real-time power grid data, perform power flow calculations to obtain the reference operating point. The voltage of each node below Then, for each device k in the set of control variables U, at its baseline value... Apply a small perturbation to it. (For example, OLTC changes by one tap, CB switches one group, and UPFC reactive power output changes by a small amount), while keeping all other control variables unchanged.

[0136] 2. Perform power flow calculation after disturbance: For each disturbance scenario of each device k, perform power flow calculation again to obtain the node voltage after disturbance. .

[0137] 3. Trajectory sensitivity calculation: Control variable of the computing device k Trajectory sensitivity to node voltage V:

[0138] (8)

[0139] 4. Calculate the comprehensive trajectory sensitivity index. And normalize it:

[0140] (9)

[0141] Here, The reference values ​​for each control variable (such as the maximum tap position of OLTC, the maximum capacity of CB, the reactive power capacity reference of UPFC, etc.). This serves as the system voltage reference value. Ultimately, the normalized trajectory sensitivity index for each device k can be obtained. .

[0142] Step 3: Adjust the normalized trajectory sensitivity index Dynamically converted into unit capacity cost weight parameters

[0143] This step is crucial in connecting sensitivity calculation with the optimization model, aiming to use the calculated performance index, i.e., the normalized trajectory sensitivity index, as the key. Static cost weight parameters in dynamic correction formulas (5)-(7) .

[0144] Unit capacity cost weighting parameter for equipment k Should be weighted by its static cost Inversely proportional to its trajectory sensitivity index (Representing voltage regulation efficiency) is directly proportional. A preferred implementation is:

[0145] (10)

[0146] : The original static cost weight parameter of equipment k.

[0147] : Normalized trajectory sensitivity index of device k.

[0148] The highest trajectory sensitivity index among all devices, used here for relative performance comparison. It can be regarded as the relative efficiency coefficient of equipment k (the value is between 0 and 1).

[0149] β: A balancing factor between 0 and 1 (e.g., 0.2 to 0.5) used to set the trade-off between static costs and dynamic performance. The smaller the β, the greater the dynamic adjustment.

[0150] This formula indicates that when device k has a high voltage regulation efficiency (relative efficiency coefficient close to 1) under its current operating state, its dynamic cost weight... It will approach or even fall below the static value. This encourages the use of high-performance equipment during optimization. Conversely, if a device is inefficient, its dynamic cost weight will be increased, reducing its usage priority.

[0151] Step 4: Integration and Iterative Solution

[0152] In each reactive power optimization cycle (or the iteration process of the IGDT robust optimization model):

[0153] 1. Perform steps one through three to calculate the dynamic cost weight parameters of each reactive power compensation device based on the current system state. .

[0154] 2. Substitute these dynamic weights into the cost term formulas (5), (6), and (7) of the deterministic optimization model, replacing the original static weights. .

[0155] 3. Use an optimization solver (such as Gurobi) to solve the updated deterministic model (or IGDT robust model) to obtain new scheduling instructions.

[0156] 2) Constraints

[0157] Equality constraints

[0158] Each node must satisfy the active and reactive power balance constraints, as shown below:

[0159] (8)

[0160] (9)

[0161] In the formula, , Let be the active and reactive power output of the generator at node i at time t; , Let be the active and reactive power outputs of renewable energy at node i at time t; , Let be the active and reactive loads of node i at time t; Let be the susceptance between nodes i and j at time t.

[0162] Inequality constraints:

[0163] The constraints on node voltage, generator terminal voltage, generator output, new energy unit output, and reactive power compensation device are as follows:

[0164] (15)

[0165] In the formula, , and The values ​​in the table are the lower voltage limit, upper voltage limit, and voltage value of node i at time t, respectively.

[0166] (16)

[0167] In the formula, , and The values ​​of the generator terminal voltage are the lower limit, upper limit, and the generator terminal voltage at time t, respectively.

[0168] (17)

[0169] In the formula, , and The values ​​in denoted as the lower limit, upper limit, and apparent power of generator unit i at time t are respectively the lower limit, upper limit, and apparent power value of the unit at time t.

[0170] (18)

[0171] In the formula, , and The values ​​in denoted as the lower limit, upper limit, and apparent power of renewable energy unit i at time t are respectively the lower limit, upper limit, and apparent power value of the unit at time t.

[0172] (19)

[0173] In the formula, , and The values ​​in denoted as i represent the lower limit, upper limit, and reactive power compensation value of reactive power compensation device i at time t, respectively.

[0174] The optimal solution obtained from the above deterministic model is: .

[0175] An uncertainty vector is defined based on the aforementioned uncertainty sources; the uncertainty vector is composed of the active / reactive power output of renewable energy sources and the active / reactive power of loads in the power system;

[0176] The uncertainty set contains all uncertainty vectors that satisfy the following condition: the absolute value of the relative deviation between the actual value and the corresponding predicted value of each uncertainty parameter in the set is not greater than a given fluctuation range parameter.

[0177] Specifically, the set of uncertainties in this method is defined as follows:

[0178] Define uncertainty vector Where P represents active power, Q represents reactive power, n represents the number of generators, m represents the number of reactive power compensation devices, L represents the number of system branches, N represents the number of system nodes, and T represents the transpose operation; its predicted value vector and fluctuation range vector are respectively and ,thereby:

[0179] (20)

[0180] In the formula, For an uncertain set, Let be the uncertainty vector of node i. Let i be the fluctuation range of node i; It is the input value, and These are variables determined in the decision-making process, and their structure is as follows: Figure 3 As shown.

[0181] The predicted value vector represents the best estimate of the future system state (new energy power generation, user electricity consumption) based on the best available information (such as weather forecasts and historical patterns). The predicted value vector is usually obtained by existing advanced application software or cloud platforms of the power system, and the results are provided as known parameters to subsequent steps.

[0182] A simplified explanation of the uncertainty model is as follows.

[0183] The robust optimization model of IGDT in this method is defined as follows:

[0184] 1) Objective function

[0185] Find the most vulnerable part among all sources of uncertainty and maximize its tolerance for minimal fluctuations.

[0186] (twenty one)

[0187] Here, "easiest" refers to the situation within a given range of uncertainty. This causes the system node voltage to... The parameter combination that reaches the system operating limit fastest Mathematically, this is expressed as in all possible The goal is to find the point where the system voltage exceeds the limit the most. For example, if the photovoltaic output is zero at a certain moment while the load power increases significantly, this combination will cause the voltage to exceed the lower limit. Conversely, if the photovoltaic output reaches its peak while the load power is at its lowest point, this combination will cause the voltage to exceed the upper limit.

[0188] 2) Constraints

[0189] System performance robustness constraints:

[0190] (twenty two)

[0191] In the formula, This is the deviation factor or risk tolerance, i.e., the degree to which the expected target deviates from the benchmark value. Constraint (22) indicates that even under the most unfavorable uncertainty, the loss must be guaranteed not to exceed an acceptable safety limit.

[0192] Voltage safety robust constraints:

[0193] (twenty three)

[0194] In the formula, For decision variables; To obtain the voltage value of node i at time t.

[0195] The remaining safety constraints are consistent with those in the deterministic model and will not be repeated here.

[0196] The solution method for the robust optimization model in this approach is as follows:

[0197] As can be seen from the above model, the maximum control cost of the robust optimization model cannot be directly calculated and depends on the fluctuation range of the uncertain variables, which is the optimization objective of the model. Therefore, a two-level optimization model needs to be established. The upper-level model is used to solve for the fluctuation range of the uncertain variables when the expected loss is guaranteed, and the lower-level model is used to solve for the loss value given the fluctuation range of the uncertain variables.

[0198] The widely used method is to derive the first-order necessary optimality conditions of the lower-level model, i.e., the KKT (Karushe-Kuhne-Tucker) conditions, and then use the constraints of the upper-level model to represent the lower-level model. The reactive power optimization scheduling problem mentioned in this invention is a nonlinear programming problem, making it difficult to obtain its KKT conditions, i.e., it cannot be solved using the above method. Therefore, this invention employs a bisection method to iteratively solve the robust optimization model.

[0199] The specific steps are as follows.

[0200] 1) Substitute the predicted values ​​of the uncertain variables into the deterministic model and use the Gurobi solver to obtain the comprehensive optimal solution for reactive power optimization of the system. .

[0201] 2) Set a bias factor based on the decision-maker's risk preference. The degree of fluctuation of the uncertain variable is calculated as follows: Expected solution at time .

[0202] 3) Robust model The feasible domain is The bisection method is used for iterative solution, with a convergence accuracy set at . The first iteration selects a trial fluctuation range. Substitute the values ​​and use the Gurobi solver to solve the robust optimization model, obtaining the optimal solution for the optimization synthesis. ,Compare and The size relationship between the two, if ≤ This indicates the current situation. It's safe; you can try a larger one. ,make Conversely, let Then proceed with the next iteration until the accuracy requirement is met: .

[0203] 4) Output the maximum fluctuation parameter. and its corresponding robust decision .

[0204] Example 2

[0205] This invention also proposes a power system risk assessment and reactive power optimization system based on IGDT, including a deterministic reactive power optimization model construction module, an uncertainty set construction module, and an IGDT robust optimization model solving module:

[0206] A deterministic reactive power optimization model construction module is used to establish a deterministic reactive power optimization model based on the power system. The deterministic reactive power optimization model takes the minimum value of the linear weighted average of the total active power loss of the system, the total voltage deviation of the system, and the total operating cost of the reactive power compensation equipment as the objective function, and sets corresponding constraints.

[0207] The uncertainty set construction module identifies uncertainty sources in the power system, constructs uncertainty sets, and sets corresponding constraints.

[0208] The IGDT robust optimization model solution module constructs an IGDT robust optimization model based on the deterministic reactive power optimization model and the uncertainty set. The objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set. The IGDT robust optimization model is iteratively solved using the bisection method to obtain the comprehensive optimal solution. The comprehensive optimal solution is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

[0209] Example 3

[0210] The present invention also proposes a terminal, including a processor and a storage medium:

[0211] The storage medium is used to store instructions;

[0212] The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0213] Example 4

[0214] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments.

[0215] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0216] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0217] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0218] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A power system risk assessment and reactive power optimization method based on IGDT, characterized in that, include: A deterministic reactive power optimization model is established based on the power system. The objective function of this model is the minimum linearly weighted sum of total system active power loss, total system voltage deviation, and total operating cost of reactive power compensation equipment, with corresponding constraints. The total operating cost of the reactive power compensation equipment refers to the operating cost of the on-load tap-changing transformer (OLTC). Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The summation is based on the corresponding unit capacity operating cost weight parameters, wherein the unit capacity operating cost weight parameters are updated each time the IGDT robust optimization model is iterated and solved; In this process, control variables are selected for OLTC devices, CB devices, and UPFC devices respectively, forming a control variable set U; based on the control variable set U, the normalized trajectory sensitivity index of each device is calculated. The normalized trajectory sensitivity index Converted into a unit capacity cost weighting parameter; Among these methods, power flow calculations are performed based on current real-time power grid data to obtain the reference operating point. The voltage of each node below For each device k in the set of control variables U, at the reference value of device k Apply perturbation Meanwhile, keeping all other control variables constant, power flow calculations are performed again for each disturbance scenario for each device k to obtain the node voltage after the disturbance. Subtract the node voltage of device k after disturbance from the voltage at the reference operating point of device k. The node voltage below The result of the subtraction is divided by the amount of small perturbation applied to device k. To obtain the control variables of device k Trajectory sensitivity to node voltage V; [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] The ratio between the corresponding reference value and the system voltage reference value is calculated, and the trajectory sensitivity is multiplied by the ratio to obtain the normalized trajectory sensitivity index for each device k. ; Identify sources of uncertainty in the power system, construct an uncertainty set, and set corresponding constraints; Based on the deterministic reactive power optimization model and the uncertainty set, an IGDT robust optimization model is constructed; the objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set. The optimal robust decision is obtained by iteratively solving the IGDT robust optimization model using a bisection method. The optimal robust decision is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

2. The power system risk assessment and reactive power optimization method based on IGDT according to claim 1, characterized in that: The operating cost of the on-load tap-changing transformer (OLTC) Reactive power compensation cost of capacitor bank CB Reactive power compensation cost of Unified Power Controller (UPFC) The specific calculation method is as follows: The unit capacity cost weighting parameter of each OLTC device is multiplied by the absolute value of the equivalent voltage regulation corresponding to the current OLTC level to obtain the first multiplication result. The first multiplication results of all OLTC devices are then added together to obtain the final result. The equivalent voltage regulation is obtained by multiplying the rated voltage change corresponding to each OLTC level by the current level, and then normalized by the system voltage sensitivity matrix with respect to reactive power. The unit capacity operating cost weight parameter of each CB device is multiplied by the absolute value of the reactive power compensated by the CB at the current moment to obtain the second multiplication result. The second multiplication results of all CB devices are then added together to obtain the final product. ; The unit capacity operating cost weight parameter of each UPFC device is multiplied by the absolute value of the reactive power output setpoint of the UPFC device at the current moment to obtain the third multiplication result. The third multiplication results of all UPFC devices are then added together to obtain the final result. .

3. The power system risk assessment and reactive power optimization method based on IGDT according to claim 1, characterized in that: The normalized trajectory sensitivity index The steps for converting the unit capacity cost weighting parameter include: Based on the normalized comprehensive trajectory sensitivity index Calculate the relative efficiency coefficient of each reactive power compensation device; the relative efficiency coefficient is the coefficient of a certain device. With the largest of all devices The ratio; For each piece of equipment, the original static cost weight parameter of the equipment is multiplied by an adjustment factor determined by the balance factor and the relative efficiency coefficient to obtain the unit capacity cost weight parameter of the equipment. The adjustment factor is inversely proportional to the relative efficiency coefficient, and the balance factor is used to set the trade-off between static cost and dynamic efficiency.

4. The power system risk assessment and reactive power optimization method based on IGDT according to claim 1, characterized in that: The specific steps for identifying uncertainty sources in the power system and constructing an uncertainty set include: An uncertainty vector is defined based on the aforementioned uncertainty sources; the uncertainty vector is composed of the active / reactive power output of renewable energy sources and the active / reactive power of loads in the power system; The uncertainty set contains all uncertainty vectors that satisfy the following condition: the absolute value of the relative deviation between the actual value and the corresponding predicted value of each uncertainty parameter in the set is not greater than a given fluctuation range parameter; the fluctuation range parameter is a non-negative decision variable. The fluctuation range parameter is a non-negative variable used to quantify the magnitude of uncertainty, and the specific value of the non-negative variable is determined by solving the IGDT robust optimization model.

5. The power system risk assessment and reactive power optimization method based on IGDT according to claim 1, characterized in that: The specific steps for iteratively solving the IGDT robust optimization model using the bisection method to obtain the comprehensive optimal solution are as follows: The predicted values ​​of the uncertain variables in the uncertainty set are used as known inputs and substituted into the deterministic reactive power optimization model for solution, thereby obtaining the optimal solution of the system under the baseline scenario. ; Set the deviation factor according to the actual situation. Based on the benchmark optimal solution The degree of fluctuation of the uncertain variable was calculated to be Expected solution at time ; The degree of fluctuation of the uncertain variable is set as follows: upper bound of the search range and the lower realm And set the convergence precision Within the search interval, iterate through the following steps to find the maximum robust uncertainty fluctuation range: The midpoint of the current search interval is taken as the trial fluctuation range for this iteration. And fix it; determine the uncertainty set based on the proposed fluctuation range; based on the uncertainty set, solve the deterministic reactive power optimization model that satisfies all constraints, and obtain the maximum possible cost under the worst uncertainty scenario. ; Compare and The size relationship between the two, if ≤ Then update the lower bound of the search interval, making = Conversely, make The next iteration will be performed based on the newly generated search interval. satisfy When the iteration stops, the maximum fluctuation parameter is output. and corresponding robust decision .

6. A power system risk assessment and reactive power optimization system based on IGDT using the method described in any one of claims 1-5, comprising a deterministic reactive power optimization model construction module, an uncertainty set construction module, and an IGDT robust optimization model solving module, characterized in that: A deterministic reactive power optimization model construction module is used to establish a deterministic reactive power optimization model based on the power system. The deterministic reactive power optimization model takes the minimum value of the linear weighted average of the total active power loss of the system, the total voltage deviation of the system, and the total operating cost of the reactive power compensation equipment as the objective function, and sets corresponding constraints. The uncertainty set construction module identifies uncertainty sources in the power system, constructs uncertainty sets, and sets corresponding constraints. The IGDT robust optimization model solution module constructs an IGDT robust optimization model based on the deterministic reactive power optimization model and the uncertainty set. The objective function of the IGDT robust optimization model is to maximize the minimum fluctuation that the uncertainty source can withstand, and corresponding constraints are set. The IGDT robust optimization model is iteratively solved using the bisection method to obtain the comprehensive optimal solution. The comprehensive optimal solution is then issued as a reactive power scheduling scheme to each reactive power control device for execution.

7. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-5.

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