Load pre-control optimization method and device

Abstract

The invention provides a load pre-control optimization method and device, which can be used in the technical field of power system operation control, and the method comprises the steps: obtaining power grid model data, a controllable load set and a controllable unit set; constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; and carrying out iterative solution by utilizing the load pre-control optimization model and a pre-constructed sensitivity calculation model, and generating a load pre-control optimization scheme conforming to a security check condition. According to the load pre-control optimization method and device provided by the invention, the solution of the high-quality load pre-control scheme is automatically completed, the calculation precision is high, the calculation process does not need manual intervention, and the problems of low efficiency and low precision of manual adjustment of the output scheme are effectively solved.

Description

Technical Field

[0001] The present application relates to the field of electronic power technology, and in particular to a load pre-control optimization method and device. Background Art

[0002] In order to accelerate the construction of a new power system and effectively ensure the consumption of new energy, the power grid dispatching and control center proposed to build a safe and stable control system for the new power system and enhance the ability to control the new power system. With the advancement of high-proportion new energy grid connection and power market reform, the stability problem of the power system is more prominent than before, and the safe and stable control system has become an indispensable means to ensure the safe and stable operation of the power grid. Among them, transient voltage stability is one of the main stability constraints for the operation mode arrangement of the interconnected power grid. It represents the ability of all nodes in the system to maintain stable voltage after the interconnected power system is subjected to a large disturbance.

[0003] At present, the load pre-control scheme for regional power grids considering transient voltage stability constraints under multiple faults mainly relies on manual experience and is repeatedly adjusted and verified through electromechanical transient simulation programs. A typical scheme is to select a power adjustment differential, divide the power of the adjustable load into multiple gears, and analyze the fault sets one by one from a certain initial point. The principle of proximity is adopted to eliminate the transient low voltage problem after the fault by reducing the load, and finally find a load pre-control scheme that meets all fault sets.

[0004] The existing technology has the following defects for actual regional power grids containing many loads:

[0005] (1) Regional power grid security and stability analysis involves multiple operating scenarios and numerous loads. The calculation content is complex and the workload is large. Therefore, manual calculation is inefficient and heavily dependent on the experience level of the calculation personnel.

[0006] (2) The adjustable power range of each load is divided into gears according to the step difference. As the step difference decreases, the combination of different gears increases exponentially. Therefore, the step difference selected by manual calculation is often large, the calculation accuracy is low, and it is difficult to find a high-quality optimization solution. Summary of the invention

[0007] In response to the problems in the prior art, the embodiments of the present application provide a load pre-control optimization method and device, which can at least partially solve the problems in the prior art.

[0008] In a first aspect, the present application provides a load pre-control optimization method, comprising:

[0009] Obtaining power grid model data, controllable load set and controllable unit set;

[0010] Constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set;

[0011] The load pre-control optimization model and the pre-built sensitivity calculation model are used to perform iterative solutions to generate a load pre-control optimization scheme that meets safety verification conditions.

[0012] Furthermore, the step of pre-building a sensitivity calculation model includes:

[0013] Obtain state variables, algebraic variables and controllable parameters of the power system;

[0014] A sensitivity calculation model is constructed according to the state variables, algebraic variables and controllable parameters.

[0015] Furthermore, the load pre-control optimization model is constructed according to the power grid model data, the controllable load set and the controllable unit set, including:

[0016] Generating an objective function with the goal of maximizing the load pre-control amount according to the controllable load set;

[0017] Generate constraint conditions according to the power grid model data, the controllable load set and the controllable unit set;

[0018] A load pre-control optimization model is constructed according to the objective function and the constraint conditions.

[0019] Further, the power grid model data includes basic power flow data and boundary condition data; the generation of constraint conditions according to the power grid model data, the controllable load set and the controllable unit set includes:

[0020] Generate power flow balance constraints and transient power angle stability constraints according to the basic power flow data, the controllable load set and the controllable unit set;

[0021] Generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints;

[0022] The constraint conditions are obtained by using the power flow balance constraint, the transient power angle stability constraint and the boundary constraint.

[0023] Furthermore, the iterative solution using the load pre-control optimization model and the sensitivity index to generate a load pre-control optimization scheme that meets the safety verification conditions includes:

[0024] Solving the sensitivity index according to the sensitivity calculation model;

[0025] Generate a load pre-control optimization scheme according to the preset continuous variables, the load pre-control optimization model and the sensitivity index;

[0026] The above operations are iteratively performed until the load pre-control optimization scheme meets the safety verification conditions.

[0027] In a second aspect, the present application provides a load pre-control optimization device, comprising:

[0028] An initial data acquisition unit, used to acquire power grid model data, a controllable load set and a controllable unit set;

[0029] A model building unit, used to build a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set;

[0030] The result generating unit is used to use the load pre-control optimization model and the pre-built sensitivity calculation model to perform iterative solution to generate a load pre-control optimization scheme that meets the safety verification conditions.

[0031] Furthermore, it also includes:

[0032] A variable acquisition unit, used to acquire state variables, algebraic variables and controllable parameters of the power system;

[0033] The calculation model building unit is used to build a sensitivity calculation model according to the state variables, algebraic variables and controllable parameters.

[0034] Furthermore, the model building unit includes:

[0035] An objective function generating module, used for generating an objective function with the objective of maximizing the load pre-control amount according to the controllable load set;

[0036] A constraint condition generating module, used for generating constraint conditions according to the power grid model data, the controllable load set and the controllable unit set;

[0037] The model building module is used to build a load pre-control optimization model according to the objective function and the constraint conditions.

[0038] Furthermore, the constraint condition generation module includes:

[0039] A first constraint generation submodule, configured to generate a power flow balance constraint and a transient power angle stability constraint according to the basic power flow data, the controllable load set and the controllable unit set;

[0040] A second constraint generation submodule is used to generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints;

[0041] The constraint condition generation submodule is used to generate constraint conditions using the power flow balance constraint, the transient power angle stability constraint and the boundary condition.

[0042] Furthermore, the result generating unit comprises:

[0043] A sensitivity calculation module, used to solve the sensitivity index according to the sensitivity calculation model;

[0044] A result generation module, used for generating a load pre-control optimization scheme according to preset continuous variables, the load pre-control optimization model and the sensitivity index;

[0045] The iterative operation module is used to iteratively execute the above operations until the load pre-control optimization scheme meets the safety verification conditions.

[0046] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the above embodiments when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0048] In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0049] The load pre-control optimization method and device provided in the present application obtain power grid model data, a controllable load set and a controllable unit set; construct a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; use the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization plan that meets safety verification conditions, thereby automatically completing the solution of high-quality load pre-control plans with high calculation accuracy, and requiring no human intervention in the calculation process, effectively solving the problems of low efficiency and low accuracy in manually adjusting output plans.

[0050] Among them, by constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set and solving it using the load pre-control optimization model and the pre-constructed sensitivity calculation model, it is possible to effectively avoid the problem of insufficient sensitivity accuracy caused by the strong nonlinearity of the power system, which in turn leads to the unavailability of the results obtained by linear programming, and realizes a reliable solution to the load pre-control scheme in the regional power grid that meets the constraints such as transient voltage stability under multiple fault sets; by using the load pre-control optimization model and the pre-constructed sensitivity calculation model for iterative solving, a load pre-control optimization scheme that meets the safety verification conditions is generated, which can ensure that the load pre-control scheme obtained in each iteration can pass the electromechanical simulation verification based on the detailed model, and converge to a higher quality solution as the number of iterations increases. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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 some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a flow chart of a load pre-control optimization method provided in an embodiment of the present application;

[0053] Figure 2 It is a flow chart of a load pre-control optimization method provided in an embodiment of the present application;

[0054] Figure 3 It is a flow chart of a load pre-control optimization method provided in an embodiment of the present application;

[0055] Figure 4 It is a flow chart of a load pre-control optimization method provided in an embodiment of the present application;

[0056] Figure 5 is a schematic diagram of a regional power grid and an external power grid provided in an embodiment of the present application;

[0057] Figure 6 It is a flow chart of a load pre-control optimization method provided in an embodiment of the present application;

[0058] Figure 7 is a structural schematic diagram of a load pre-control optimization device provided in an embodiment of the present application;

[0059] Figure 8 is a structural schematic diagram of a load pre-control optimization device provided in an embodiment of the present application;

[0060] Fig. 9is a structural schematic diagram of a load pre-control optimization device provided in an embodiment of the present application;

[0061] Fig.10 is a structural schematic diagram of a load pre-control optimization device provided in an embodiment of the present application;

[0062] Fig.11 is a structural schematic diagram of a load pre-control optimization device provided in an embodiment of the present application;

[0063] Fig.12 is a schematic diagram of the physical structure of an electronic device provided in one embodiment of the present application;

[0064] Fig.13 It is a schematic diagram of a transient voltage change trajectory provided by an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.

[0066] The following uses the server as an example of the execution subject to illustrate the specific implementation process of the load pre-control optimization method provided in the embodiment of the present application.

[0067] Figure 1 is a flow chart of a load pre-control optimization method provided by an embodiment of the present application, such as Figure 1 As shown, the load pre-control optimization method provided by the present application includes:

[0068] S101: Obtaining power grid model data, a controllable load set, and a controllable unit set;

[0069] S102: constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set;

[0070] S103: using the load pre-control optimization model and the pre-built sensitivity calculation model to perform iterative solution to generate a load pre-control optimization solution that meets safety verification conditions.

[0071] from Figure 1It can be seen from the shown process that the load pre-control optimization method provided by the present application obtains power grid model data, a controllable load set and a controllable unit set; constructs a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; uses the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization scheme that meets the safety verification conditions, thereby realizing automatic completion of the solution of high-quality load pre-control schemes with high calculation accuracy, and requiring no manual intervention in the calculation process, which effectively solves the problems of low efficiency and low precision of manual adjustment of output schemes.

[0072] Each step is explained in detail below.

[0073] S101: Obtaining power grid model data, a controllable load set, and a controllable unit set;

[0074] Specifically, the server obtains the power grid model data, the set of controllable loads that can be controlled in the region, and the set of controllable units with external power reduction in the region (the set of adjustable generators in the external power grid). Among them, the set of controllable loads in the region specifies the controllable loads to be analyzed and their adjustable amounts, and the set of controllable units with external power reduction in the region is used to offset the load reduction in the region and maintain the basic balance of power generation and load in the system.

[0075] In one embodiment, the power grid model data includes basic power flow data, boundary condition data, stability calculation data, fault set data, etc., which are used for power flow calculation and transient stability simulation.

[0076] S102: constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set;

[0077] Specifically, considering that the system load demand remains unchanged, during the adjustment of the regional power grid load pre-control, it is necessary to reduce the output of the units designated by the external power grid to maintain the system power generation and load balance. Figure 5 As shown, the server is set to D I is the set of controllable loads in the area, G E is the set of adjustable generators in the external power grid, according to the power grid model data and the set of controllable loads in the region D I , External grid adjustable generator set G E Construct a load pre-control optimization model for the regional power grid.

[0078] In one embodiment, the power grid model data includes basic flow data, boundary condition data, stability calculation data and fault set data. Before constructing the load pre-control optimization model of the regional power grid, the server obtains the initial operating point based on the power grid model data, and then scans the fault set to screen out the fault set of transient voltage instability to perform transient stability analysis under the initial output. If the power grid is in a stable state, there is no need to perform subsequent load pre-control operations.

[0079] In one embodiment, the server performs transient stability analysis on the power grid through simulation software, and the transient voltage stability calculation adopts electromechanical transient simulation. For the DC point power grid, when the DC response characteristics have a greater impact on the system voltage stability, electromechanical-electromagnetic transient hybrid simulation or full electromagnetic simulation verification is adopted; when it is necessary to consider the long-term component dynamic characteristics such as unit overexcitation, long-term dynamic simulation is adopted.

[0080] Figure 2 is a flow chart of a load pre-control optimization method provided by an embodiment of the present application, such as Figure 2 As shown, S102 includes:

[0081] S201: generating an objective function with the goal of maximizing the load pre-control amount according to the controllable load set;

[0082] Specifically, the server aims to maximize the sum of the load pre-control quantities of the regional power grid and generates an objective function to ensure that the system restores voltage stability after the load pre-control scheme is executed. The objective function is expressed as follows:

[0083]

[0084] Where D I represents the controllable load set in the area, ΔP d,i Represents the load active power pre-control amount of node i.

[0085] S202: generating constraint conditions according to the power grid model data, the controllable load set and the controllable unit set;

[0086] Specifically, the server generates constraints to build a load pre-control optimization model in combination with the objective function. The constraints are generated based on the power grid model data, the controllable load set and the controllable unit set, wherein the power grid model data includes basic flow data and boundary condition data.

[0087] Figure 3 is a flow chart of a load pre-control optimization method provided by an embodiment of the present application, such as Figure 3 As shown, S202 includes:

[0088] S301: generating a power flow balance constraint and a transient power angle stability constraint according to the basic power flow data, the controllable load set and the controllable unit set;

[0089] Specifically, the server generates a power flow balance constraint based on the power grid model data, the controllable load set and the controllable unit set. The power flow balance constraint is expressed as follows:

[0090]

[0091] Where N is the total number of nodes, P g,i and Q g,i are the active power and reactive power of node i, respectively, d,i and Q d,i are the load active and load reactive of node i, V i and V j are the voltage amplitudes of nodes i and j respectively, θ ij is the phase angle between node i and node j, G ij and B ij are the real and imaginary parts of the admittance matrix elements of node i and node j, respectively, ΔP d,i represents the load active power pre-control quantity of node i, η d,i It represents the load power factor of node i in the base state. During the load pre-control process, it is assumed that the reactive power is adjusted synchronously with the active power according to a constant power factor.

[0092] The server obtains transient voltage stability indicators based on the power grid model data, controllable load set and controllable unit set to generate transient power angle stability constraints. The transient voltage stability indicators include indicators reflecting the voltage drop degree, voltage fluctuation degree and voltage recovery level. When the amplitude of the voltage curve fluctuation is larger and the fluctuation duration is longer, the voltage fluctuation degree is larger, which means the transient voltage stability of the node is worse; when the steady-state voltage level after the fault is closer to the steady-state value before the fault, and the time to enter the steady state after the fault is shorter, the node voltage has a stronger ability to recover the steady state.

[0093] The server uses the duration of the voltage amplitude of the low-voltage node after the fault being lower than the voltage recovery threshold as the transient voltage indicator, which is expressed as:

[0094] T VS,i =T(V i (t) <V th ) (4)

[0095] Where V i (t) represents the voltage amplitude of node i at time t, V th Indicates the voltage recovery threshold, T VS,iIt represents the transient low voltage duration of node i, that is, the total time that the node voltage amplitude is lower than the voltage recovery threshold during the simulation period.

[0096] In order to characterize the severity of low voltage at the node, the server introduces the transient low voltage depth and transient low voltage area S LV,i As a transient voltage indicator. Among them, the transient low voltage depth is the maximum amplitude of the node voltage below the voltage recovery threshold after the fault, expressed as:

[0097]

[0098] Where V i (t) represents the voltage amplitude of node i at time t, V th Indicates the voltage recovery threshold, ΔV LV,i is the magnitude by which the node voltage after a fault is lower than the voltage recovery threshold.

[0099] Transient low voltage area S LV,i The area where the node voltage after the fault is lower than the voltage recovery threshold is expressed as:

[0100]

[0101] Where T represents the duration of transient low voltage, N is the total number of nodes, V i (t) represents the voltage amplitude of node i at time t, V th Indicates the voltage recovery threshold, sign(V i (t)) is a symbolic function:

[0102]

[0103] In one embodiment, during the transient process after the power system is disturbed, the load node voltage should be able to recover to above 0.80 (per unit) within 10 seconds. During the long-term process after the power system is disturbed, the load node voltage should be able to maintain or recover to above 0.90 (per unit).

[0104] In one embodiment, Fig.13 is a schematic diagram of transient voltage change trajectory, such as Fig.13 As shown, V i 1 (t) and V i 2 (t) are the transient voltage change trajectories in two cases, V th =0.9 is the voltage recovery threshold.

[0105] S302: Generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints;

[0106] Specifically, the server generates the active output constraint and reactive output constraint of the generator according to the basic power flow data, boundary condition data and the set of controllable units, which can be expressed as:

[0107]

[0108]

[0109] Where P g,i and Q g,i are the active power and reactive power of node i, and are the lower and upper limits of the active power generated by node i, and are the lower and upper limits of the reactive power generated by node i.

[0110] The server generates node voltage constraints based on basic power flow data, boundary condition data, controllable load set and controllable unit set, which can be expressed as:

[0111] V i min ≤V i ≤V i max (10)

[0112] Where V i are the voltage amplitude of node i, V i min and V i max are the lower and upper limits of the voltage amplitude at node i respectively.

[0113] The server generates line and transformer thermal stability constraints based on basic power flow data, boundary condition data, controllable load set and controllable unit set, which are expressed as:

[0114]

[0115] Where P ij and P ji are the active powers at the beginning and end of branch ij, and are the lower and upper limits of the active power of branch ij respectively.

[0116] The server generates node load pre-control quantity constraints based on basic power flow data, boundary condition data and controllable load set, which can be expressed as:

[0117]

[0118] In the formula, ΔP d,i represents the load active power pre-control quantity of node i, and They are respectively the lower limit and upper limit of the load active power pre-control quantity.

[0119] S303: Obtain constraint conditions using the power flow balance constraint, the transient power angle stability constraint and the boundary constraint.

[0120] Specifically, after the server generates power flow balance constraints, transient power angle stability constraints and boundary constraints, it combines the power flow balance constraints, transient power angle stability constraints and boundary constraints to obtain constraint conditions. The boundary constraints include the active output constraints of the generator, the reactive output constraints of the generator, the node voltage constraints, the thermal stability constraints of the line and transformer, and the node load pre-control constraints.

[0121] S203: Constructing a load pre-control optimization model according to the objective function and the constraint conditions.

[0122] Specifically, after the server generates the objective function and constraints, it combines the objective function and constraints to construct a load pre-control optimization model, where the constraints include power flow balance constraints, transient power angle stability constraints and boundary constraints. The boundary constraints include the active output constraints of the generator, the reactive output constraints of the generator, the node voltage constraints, the thermal stability constraints of the line and transformer, and the node load pre-control quantity constraints.

[0123] S103: using the load pre-control optimization model and the pre-built sensitivity calculation model to perform iterative solution to generate a load pre-control optimization solution that meets safety verification conditions.

[0124] Specifically, the server pre-builds a sensitivity calculation model to analyze the sensitivity of transient low voltage duration, transient low voltage depth and transient low voltage area to node load power, and uses the constructed load pre-control optimization model and the pre-built sensitivity calculation model to perform calculations, and iteratively solves to generate a load pre-control optimization plan that meets safety verification conditions.

[0125] Figure 4 is a flow chart of a load pre-control optimization method provided by an embodiment of the present application, such as Figure 4 As shown, the steps of pre-building a sensitivity calculation model include:

[0126] S401: Obtain state variables, algebraic variables and controllable parameters of the power system;

[0127] Specifically, the mathematical models used in large disturbance transient voltage stability and dynamic voltage stability calculations are basically the same as those used in transient power angle stability calculations, and time domain simulation programs can be used for calculation and analysis. In the transient and dynamic processes, mathematical models of load dynamic characteristics, generators and their excitation systems and speed control systems, generator overexcitation limiting characteristics, generator strong excitation action characteristics, reactive power compensation devices, DC transmission systems, low voltage load reduction and other components and control devices should be taken into account.

[0128] The mathematical model for transient voltage stability analysis of power systems can be described by a set of differential-algebraic equations:

[0129]

[0130] 0=g(x,y,μ) (15)

[0131] Among them, x represents the state variable of the system; y represents the algebraic variable of the system; μ represents the controllable parameter.

[0132] The server obtains state variables, algebraic variables and controllable parameters of the power system to generate sensitivity indicators according to the state variables, algebraic variables and controllable parameters of the power system.

[0133] In one embodiment, the state variables of the system include the generator power angle and speed, the algebraic variables of the system include the node voltage amplitude and phase angle, and the controllable parameters include the generator output and load power, etc.

[0134] S402: Constructing a sensitivity calculation model according to the state variables, algebraic variables and controllable parameters.

[0135] Specifically, the server constructs a sensitivity calculation model according to state variables, algebraic variables and controllable parameters to solve the sensitivity index according to the sensitivity calculation model.

[0136] For the power system described by equations (14)-(15), the system trajectory contains the stability information of the system. The trajectory sensitivity can reflect the degree of change of the system operation trajectory caused by a small change in the parameters. The sensitivity of the transient low voltage duration, transient low voltage depth and transient low voltage area to the node load power just reveals the impact of the node load power change on the power system operation trajectory and even the safety and stability.

[0137] The server builds a sensitivity calculation model based on state variables, algebraic variables and controllable parameters:

[0138]

[0139] In the formula, x0 is the initial value of x, x represents the state variable of the system, y represents the algebraic variable of the system, μ represents the controllable parameter, x(t) and y(t) represent the trajectories of the state variable x and algebraic variable y over time, Δμ represents the perturbation of parameter μ, Δx(t) and Δy(t) represent the changes at time t caused by the parameter perturbation, and x μ (t) and y μ (t) is the trajectory sensitivity, which characterizes the changes in state variables and algebraic variables caused by the change of parameter μ.

[0140] In one embodiment, the server uses a parameter perturbation method to solve the sensitivity index through numerical difference based on an electromechanical simulation program.

[0141] Figure 6 is a flow chart of a load pre-control optimization method provided by an embodiment of the present application, such as Figure 6 As shown, S103 includes:

[0142] S601: solving a sensitivity index according to the sensitivity calculation model;

[0143] Specifically, due to the strong nonlinearity of the power system, it is difficult to achieve load domain control based on sensitivity in one step. It usually takes several iterations to obtain an optimization solution that meets the given accuracy requirements. Therefore, the sensitivity needs to be updated based on the new operating point after each step forward in order to find the direction of the next optimization adjustment. In each round of iteration, the server uses the sensitivity calculation model described by equations (16)-(17) to solve the sensitivity index and obtain the trajectory sensitivity to achieve load pre-control optimization.

[0144] In one embodiment, the server uses the same method to calculate node voltage sensitivity, generator reactive output sensitivity, and line active power sensitivity, etc. Node voltage sensitivity, generator reactive output sensitivity, and line active power sensitivity can also be calculated using an analytical method.

[0145] S602: generating a load pre-control optimization scheme according to the preset continuous variable, the load pre-control optimization model and the sensitivity index;

[0146] Specifically, the server generates a load pre-control optimization scheme based on the preset continuous variables, load pre-control optimization model and sensitivity index. Since transient voltage stability analysis involves the numerical integration of differential-algebraic equations, the above maximum load pre-control scheme optimization model is difficult to solve directly, so the server introduces the variable ΔP g,m =P g,m -P g0,m It represents the output adjustment of the externally reduced unit, and constructs the load pre-control and the corresponding external unit output adjustment plan through gradual linearization:

[0147]

[0148]

[0149] Due to the strong nonlinearity of the power system, the load pre-control and external unit output adjustment scheme ΔP obtained based on the calculated trajectory sensitivity and linear programming d,k and ΔP g,m It may not be able to pass the safety check based on the electromechanical simulation program. Therefore, the server introduces the continuous variable λ, and the output of the internal and external units in the area are:

[0150]

[0151] Where P g,m is the active power generated by node m, P g0,m P g,m The initial value of P d,k is the load active power of node k, P d0,k P d,k The initial value, ΔP d,k and ΔP g,m They are the directions of load pre-control and external unit reduction plans respectively.

[0152] The continuous variable λ starts from the initial value 1.0 and decreases according to its step size. The λ that satisfies equations (25) and (19)-(24) can be quickly determined. cr value.

[0153] In one embodiment, the step size of the continuous variable λ may be a preset fixed step size or an adaptive step size.

[0154] S603: Iteratively execute the above operations until the load pre-control optimization scheme meets the safety verification conditions.

[0155] Specifically, the server further iterates the above solution process to obtain a more accurate load pre-control and external unit reduction plan. and After obtaining, a new starting point and Can be updated to:

[0156]

[0157] Repeat the above calculation process until The value of satisfies the termination condition, that is, load pre-control and external unit reduction plan and The safety checks of the corresponding operating points can all pass.

[0158] The present application provides a load pre-control optimization method, which obtains power grid model data, a controllable load set and a controllable unit set; constructs a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; uses the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization plan that meets safety verification conditions, thereby automatically completing the solution of high-quality load pre-control plans with high calculation accuracy, and requiring no manual intervention in the calculation process, effectively solving the problems of low efficiency and low precision in manually adjusting output plans.

[0159] Among them, by constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set and solving it using the load pre-control optimization model and the pre-constructed sensitivity calculation model, it is possible to effectively avoid the problem of insufficient sensitivity accuracy caused by the strong nonlinearity of the power system, which in turn leads to the unavailability of the results obtained by linear programming, and realizes a reliable solution to the load pre-control scheme in the regional power grid that meets the constraints such as transient voltage stability under multiple fault sets; by using the load pre-control optimization model and the pre-constructed sensitivity calculation model for iterative solving, a load pre-control optimization scheme that meets the safety verification conditions is generated, which can ensure that the load pre-control scheme obtained in each iteration can pass the electromechanical simulation verification based on the detailed model, and converge to a higher quality solution as the number of iterations increases.

[0160] Based on the same inventive concept, the embodiments of the present application also provide a load pre-control optimization device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of solving the problem by the load pre-control optimization device is similar to that of the load pre-control optimization method, the implementation of the load pre-control optimization device can refer to the implementation of the method based on software performance benchmark determination, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0161] Figure 7 is a schematic diagram of the structure of a load pre-control optimization device provided in an embodiment of the present application, such as Figure 7 As shown, the device comprises:

[0162] The initial data acquisition unit 701 is used to acquire the power grid model data, the controllable load set and the controllable unit set;

[0163] Specifically, the initial data acquisition unit 701 acquires the power grid model data, the controllable load set that can be controlled in the region, and the controllable unit set of the external power reduction in the region (the external power grid adjustable generator set). Among them, the controllable load set in the region specifies the controllable load to be analyzed and its adjustable amount, and the controllable unit set of the external power reduction in the region is used to offset the load reduction in the region and maintain the basic balance of power generation and load of the system.

[0164] A model building unit 702, configured to build a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set;

[0165] Specifically, considering that the system load demand remains unchanged, during the adjustment of the regional power grid load pre-control, it is necessary to reduce the output of the units designated by the external power grid to maintain the system power generation and load balance. Figure 5 As shown, the model building unit 702 is set to I is the set of controllable loads in the area, G E is the set of adjustable generators in the external power grid, based on the power grid model data and the set of controllable loads in the region D I , External grid adjustable generator set G E Construct a load pre-control optimization model for the regional power grid.

[0166] The result generating unit 703 is used to perform iterative solution using the load pre-control optimization model and the pre-built sensitivity calculation model to generate a load pre-control optimization solution that meets the safety verification conditions.

[0167] Specifically, the server pre-builds a sensitivity calculation model to analyze the sensitivity of transient low voltage duration, transient low voltage depth and transient low voltage area to node load power. The result generation unit 703 uses the constructed load pre-control optimization model and the pre-built sensitivity calculation model to perform calculations, and iteratively solves to generate a load pre-control optimization plan that meets the safety verification conditions.

[0168] Figure 8 is a schematic diagram of the structure of a load pre-control optimization device provided in an embodiment of the present application. Figure 7 Based on the embodiment, further, Figure 8 As shown, the load pre-control optimization device provided by the present application also includes:

[0169] The variable acquisition unit 801 is used to acquire the state variables, algebraic variables and controllable parameters of the power system;

[0170] The calculation model building unit 802 is used to build a sensitivity calculation model according to the state variables, algebraic variables and controllable parameters.

[0171] Fig. 9is a schematic diagram of the structure of a load pre-control optimization device provided in an embodiment of the present application. Figure 7 Based on the embodiment, further, Fig. 9 As shown, the load pre-control optimization device provided by the present application also includes:

[0172] An objective function generating module 901 is used for generating an objective function with the objective of maximizing the load pre-control amount according to the controllable load set;

[0173] A constraint condition generating module 902, configured to generate constraint conditions according to the power grid model data, the controllable load set and the controllable unit set;

[0174] The model building module 903 is used to build a load pre-control optimization model according to the objective function and the constraint conditions.

[0175] Fig.10 is a schematic diagram of the structure of a load pre-control optimization device provided in an embodiment of the present application. Fig. 9 Based on the embodiment, further, Fig.10 As shown, the load pre-control optimization device provided by the present application also includes:

[0176] The first constraint generation submodule 1001 is used to generate a power flow balance constraint and a transient power angle stability constraint according to the basic power flow data, the controllable load set and the controllable unit set;

[0177] The second constraint generation submodule 1002 is used to generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints;

[0178] The constraint condition generation submodule 1003 is used to generate constraint conditions using the power flow balance constraint, the transient power angle stability constraint and the boundary condition.

[0179] Fig.11 is a schematic diagram of the structure of a load pre-control optimization device provided in an embodiment of the present application. Figure 7 Based on the embodiment, further, Fig.11 As shown, the load pre-control optimization device provided by the present application also includes:

[0180] A sensitivity calculation module 1101 is used to solve the sensitivity index according to the sensitivity calculation model;

[0181] A result generation module 1102 is used to generate a load pre-control optimization scheme according to preset continuous variables, the load pre-control optimization model and the sensitivity index;

[0182] The iterative operation module 1103 is used to iteratively execute the above operations until the load pre-control optimization scheme meets the safety verification conditions.

[0183] The present application provides a load pre-control optimization method and device, which obtains power grid model data, a controllable load set and a controllable unit set; constructs a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; uses the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization plan that meets safety verification conditions, thereby automatically completing the solution of high-quality load pre-control plans with high calculation accuracy, and does not require manual intervention in the calculation process, effectively solving the problems of low efficiency and low accuracy of manual adjustment of output plans.

[0184] Among them, by constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set and solving it using the load pre-control optimization model and the pre-constructed sensitivity calculation model, it is possible to effectively avoid the problem of insufficient sensitivity accuracy caused by the strong nonlinearity of the power system, which in turn leads to the unavailability of the results obtained by linear programming, and realizes a reliable solution to the load pre-control scheme in the regional power grid that meets the constraints such as transient voltage stability under multiple fault sets; by using the load pre-control optimization model and the pre-constructed sensitivity calculation model for iterative solving, a load pre-control optimization scheme that meets the safety verification conditions is generated, which can ensure that the load pre-control scheme obtained in each iteration can pass the electromechanical simulation verification based on the detailed model, and converge to a higher quality solution as the number of iterations increases.

[0185] Fig.12 is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, such as Fig.12 As shown, the electronic device may include: a processor 1201, a communication interface 1202, a memory 1203 and a communication bus 1204, wherein the processor 1201, the communication interface 1202 and the memory 1203 communicate with each other through the communication bus 1204. The processor 1201 may call the logic instructions in the memory 1203 to execute the following method: obtaining the power grid model data, the controllable load set and the controllable unit set; constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; using the load pre-control optimization model and the pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization scheme that meets the safety verification conditions.

[0186] In addition, the logic instructions in the above-mentioned memory 1203 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0187] The present embodiment discloses a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining power grid model data, a controllable load set and a controllable unit set; constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; using the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization scheme that meets safety verification conditions.

[0188] The present embodiment provides a computer-readable storage medium, which stores a computer program. The computer program enables the computer to execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining power grid model data, a controllable load set and a controllable unit set; constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; using the load pre-control optimization model and a pre-constructed sensitivity calculation model to perform iterative solution to generate a load pre-control optimization plan that meets safety verification conditions.

[0189] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0190] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0193] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0194] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A load pre-control optimization method, characterized in that: include: Obtaining power grid model data, controllable load set and controllable unit set; Constructing a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; The load pre-control optimization model and the pre-built sensitivity calculation model are used to perform iterative solutions to generate a load pre-control optimization scheme that meets safety verification conditions.

2. The load pre-control optimization method according to claim 1, characterized in that: The steps to pre-build the sensitivity calculation model include: Obtain state variables, algebraic variables and controllable parameters of the power system; A sensitivity calculation model is constructed according to the state variables, algebraic variables and controllable parameters.

3. The load pre-control optimization method according to claim 1, characterized in that: The constructing of a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set includes: Generating an objective function with the goal of maximizing the load pre-control amount according to the controllable load set; Generate constraint conditions according to the power grid model data, the controllable load set and the controllable unit set; A load pre-control optimization model is constructed according to the objective function and the constraint conditions.

4. The load pre-control optimization method according to claim 3, characterized in that: The power grid model data includes basic power flow data and boundary condition data; the constraint conditions generated according to the power grid model data, the controllable load set and the controllable unit set include: Generate power flow balance constraints and transient power angle stability constraints according to the basic power flow data, the controllable load set and the controllable unit set; Generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints; The constraint conditions are obtained by using the power flow balance constraint, the transient power angle stability constraint and the boundary constraint.

5. The load pre-control optimization method according to claim 1, characterized in that: The load pre-control optimization model and the pre-built sensitivity calculation model are used to iteratively solve and generate a load pre-control optimization scheme that meets the safety verification conditions, including: Solving the sensitivity index according to the sensitivity calculation model; Generate a load pre-control optimization scheme according to the preset continuous variables, the load pre-control optimization model and the sensitivity index; The above operations are iteratively performed until the load pre-control optimization scheme meets the safety verification conditions.

6. A load pre-control optimization device, characterized in that: include: An initial data acquisition unit, used to acquire power grid model data, a controllable load set and a controllable unit set; A model building unit, used to build a load pre-control optimization model according to the power grid model data, the controllable load set and the controllable unit set; The result generating unit is used to use the load pre-control optimization model and the pre-built sensitivity calculation model to perform iterative solution to generate a load pre-control optimization scheme that meets the safety verification conditions.

7. The load pre-control optimization device according to claim 6, characterized in that: Also includes: A variable acquisition unit, used to acquire state variables, algebraic variables and controllable parameters of the power system; The calculation model building unit is used to build a sensitivity calculation model according to the state variables, algebraic variables and controllable parameters.

8. The load pre-control optimization device according to claim 6, characterized in that: The model building unit comprises: An objective function generating module, used for generating an objective function with the objective of maximizing the load pre-control amount according to the controllable load set; A constraint condition generating module, used for generating constraint conditions according to the power grid model data, the controllable load set and the controllable unit set; The model building module is used to build a load pre-control optimization model according to the objective function and the constraint conditions.

9. The load pre-control optimization device according to claim 8, characterized in that: The power grid model data includes basic flow data and boundary condition data; the constraint condition generation module includes: A first constraint generation submodule, configured to generate a power flow balance constraint and a transient power angle stability constraint according to the basic power flow data, the controllable load set and the controllable unit set; A second constraint generation submodule is used to generate boundary constraints according to the basic power flow data, the boundary condition data, the controllable load set and the controllable unit set; the boundary constraints include: active output constraints, reactive output constraints, node voltage constraints, thermal stability constraints and load pre-control constraints; The constraint condition generation submodule is used to generate constraint conditions using the power flow balance constraint, the transient power angle stability constraint and the boundary condition.

10. The load pre-control optimization device according to claim 6, characterized in that: The result generating unit comprises: A sensitivity calculation module, used to solve the sensitivity index according to the sensitivity calculation model; A result generation module, used for generating a load pre-control optimization scheme according to preset continuous variables, the load pre-control optimization model and the sensitivity index; The iterative operation module is used to iteratively execute the above operations until the load pre-control optimization scheme meets the safety verification conditions.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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