Equivalent model construction method and system considering multi-type reactive power resource operation characteristics

Abstract

The invention provides an equivalent model construction method and system considering multi-type reactive power resource operation characteristics, and belongs to the technical field of power system numerical simulation. According to the method, multiple types of reactive power resources are classified according to operation characteristics of different reactive power resources; and determining a sensitivity value of each reactive power resource equivalent model, screening out a dominant parameter most sensitive to voltage change in all controllable parameters of the equivalent model, and solving the dominant parameter of the equivalent model by taking the minimum comprehensive deviation of voltage fluctuation and reactive power output as a target function to obtain a multi-type reactive power resource equivalent model. According to the method, the equivalent modeling precision of multiple types of reactive power resources can be improved, and technical support is provided for reactive power regulation and voltage control of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system numerical simulation, and particularly relates to a method and system for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources. Background Art

[0002] With the large-scale access of new power equipment in the power system, the sources and consumption methods of reactive power in the power grid have become increasingly complex. Traditional reactive power regulation mainly relies on equipment such as synchronous generators, shunt capacitors, and static var compensation devices (SVC, STATCOM, etc.). However, in the context of a high proportion of new energy, the large-scale access of distributed power sources such as photovoltaic power stations, wind farms, energy storage power stations, and electric vehicle charging piles has significantly changed the dynamic response characteristics and spatial distribution characteristics of reactive power resources. At the same time, widely existing inductive and capacitive loads such as lighting equipment and air conditioners also pose new challenges to the reactive power management of the power grid.

[0003] Different types of reactive power resources have their own operating modes and control characteristics. Existing equivalent modeling methods for reactive power resources generally group the target reactive power resources based on the idea of "coherent equivalence", and the parameters of the equivalent model representing each group can be obtained by the capacity weighting method. This method mainly relies on empirical formulas or test data of a single device, and fails to fully consider the dynamic characteristics of multiple types of reactive power resources and the sensitivity of control parameters, resulting in limited model accuracy. Especially in the context of the gradually increasing proportion of new energy, the time-varying characteristics of reactive power resources and their impact on the power grid voltage are becoming increasingly significant. There is an urgent need for an equivalent modeling method that can take into account the operating characteristics of different reactive power resources and optimize key parameters. Summary of the Invention

[0004] The present invention provides a method and system for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources to improve the modeling accuracy of multiple types of reactive power resources.

[0005] In a first aspect, the present invention provides a method for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources, including:

[0006] Classify multiple reactive power source and consumption devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consumption device;

[0007] Under typical scenarios of voltage fluctuations in the target power grid, determine the sensitivity values of each equivalent model to determine the dominant parameters in each equivalent model that are most sensitive to voltage changes;

[0008] Taking the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization objective, solve the dominant parameters of each equivalent model to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

[0009] Optionally, classifying multiple reactive power sources and consuming devices in the target power grid to determine an equivalent model corresponding to each type of reactive power source or consuming device includes:

[0010] Regarding the output units that rely on rotational motion to achieve energy conversion and output reactive power control as rotational reactive power resources; the equivalent model of the rotational reactive power resources is a synchronous generator model;

[0011] Regarding the power electronic devices that are connected to the grid through converters or inverters as power electronic reactive power resources; the equivalent model of the power electronic reactive power resources is a single-stage photovoltaic model;

[0012] Regarding the loads whose consumed active or reactive power has an algebraic equation relationship with the port voltage or frequency and whose operating characteristics are linearly related to the consumed reactive power as static reactive loads; the equivalent model of the static reactive loads is a ZIP load model;

[0013] Regarding the loads whose reactive power changes with the operating state and is affected by voltage as dynamic reactive loads; the equivalent model of the dynamic reactive loads is an induction motor model;

[0014] Regarding the loads whose consumed or provided reactive power depends on the control strategy and responds to voltage changes as power electronic reactive loads; the equivalent model of the power electronic reactive loads is a energy storage model.

[0015] Optionally, in the typical scenarios of voltage fluctuations in the target power grid, determining the sensitivity values of each equivalent model to determine the dominant parameters that are most sensitive to voltage changes in each equivalent model, including:

[0016] Calculating the trajectory sensitivity of each parameter in each equivalent model according to the following formula:

[0017]

[0018] where SE j is the trajectory sensitivity corresponding to parameter j; O i is the voltage response trajectory of the target node i in the target equivalent model; θ j is the normalized value of parameter j; Δθ j is the change amount of parameter j; θ m is the normalized value of parameter m; m is the parameter space dimension; t represents the t-th sampling point;

[0019] Regarding the parameters corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as the dominant parameters.

[0020] Optionally, taking the minimum comprehensive deviation of voltage fluctuation and reactive power output as the optimization objective, solving the leading parameters of each equivalent model to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources, including:

[0021] Construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0022]

[0023] where L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node;

[0024] Construct constraint conditions:

[0025]

[0026] where x min is the lower limit of the adjustable range of the leading parameter to be identified; x max is the upper limit of the adjustable range of the leading parameter to be identified; n is the current iteration number of the optimization algorithm; N is the upper limit of the current iteration number of the optimization algorithm;

[0027] Based on the objective function F d and the constraint conditions, use the Grey Wolf Optimization algorithm to solve the leading parameters to be identified for each equivalent model, and obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

[0028] Optionally, taking the minimum comprehensive deviation of voltage fluctuation and reactive power output as the optimization objective, solving the leading parameters of each equivalent model to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources, including:

[0029] Construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0030]

[0031] where L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; Ueq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node;

[0032] Construct the constraint conditions:

[0033]

[0034] where x min is the lower limit of the adjustable range of the leading parameter to be identified; x max is the upper limit of the adjustable range of the leading parameter to be identified; F an is the fitness of the objective function corresponding to the nth optimization iteration; F amin is the minimum value of the fitness of the objective function under the condition of meeting the optimization end condition;

[0035] Based on the objective function F d and the constraint conditions, the grey wolf algorithm is used to solve the leading parameter to be identified for each equivalent model, and an equivalent model reflecting the operating characteristics of multiple types of reactive power resources is obtained.

[0036] In a second aspect, the present invention provides a system for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources, including:

[0037] A first determination module, configured to classify multiple reactive power sources and consuming devices in the target power grid to determine an equivalent model corresponding to each type of reactive power source or consuming device;

[0038] A second determination module, configured to determine the sensitivity value of each equivalent model in a typical scenario of voltage fluctuation in the target power grid to determine the leading parameter that is most sensitive to voltage change in each equivalent model;

[0039] A parameter solving module, configured to solve the leading parameter of each equivalent model with the minimum comprehensive deviation of voltage fluctuation and reactive power output as the optimization goal, and obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

[0040] Optionally, the first determination module includes:

[0041] A first determination unit, configured to use the output unit that realizes energy conversion and outputs reactive power control depending on rotational motion as the rotational reactive power resource; the equivalent model of the rotational reactive power resource is a synchronous generator model;

[0042] A second determination unit, configured to use a power electronic device that realizes grid connection operation through an inverter or a converter as a power electronic reactive resource; an equivalent model of the power electronic reactive resource is a single-stage photovoltaic model;

[0043] A third determination unit, configured to use a load whose relationship between the consumed active or reactive power and the port voltage or frequency is an algebraic equation and whose operating characteristics are linearly related to the consumed reactive power as a static reactive load; an equivalent model of the static reactive load is a ZIP load model;

[0044] A fourth determination unit, configured to use a load whose reactive power changes with the operating state and is affected by the voltage as a dynamic reactive load; an equivalent model of the dynamic reactive load is an induction motor model;

[0045] A fifth determination unit, configured to use a load whose consumed or provided reactive power depends on a control strategy and responds to voltage changes as a power electronic reactive load; an equivalent model of the power electronic reactive load is an energy storage model.

[0046] Optionally, the second determination module includes:

[0047] A first calculation unit, configured to calculate the trajectory sensitivity of each parameter in each equivalent model according to the following formula:

[0048]

[0049] where SE j is the trajectory sensitivity corresponding to parameter j; O i is the voltage response trajectory of target node i in the target equivalent model; θ j is the normalized value of parameter j; Δθ j is the change amount of parameter j; θ m is the normalized value of parameter m; m is the parameter space dimension; t represents the t-th sampling point;

[0050] A sixth determination unit, configured to use the parameter corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as a dominant parameter.

[0051] Optionally, the parameter solving module includes:

[0052] A first construction unit, configured to construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0053]

[0054] Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node;

[0055] The second construction unit is used to construct constraint conditions:

[0056]

[0057] Among them, x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; n is the current iteration number of the optimization algorithm; N is the upper limit of the current iteration number of the optimization algorithm;

[0058] The first parameter solving unit is used to solve the dominant parameter to be identified for each equivalent model based on the objective function F d and the constraint conditions, and use the grey wolf algorithm to obtain the equivalent model reflecting the operation characteristics of multiple types of reactive power resources.

[0059] Optionally, the parameter solving module includes:

[0060] The second construction unit is used to construct the objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0061]

[0062] Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node;

[0063] The third construction unit is used to construct constraint conditions:

[0064]

[0065] where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; F an is the fitness of the objective function corresponding to the nth optimization iteration; F amin is the minimum value of the fitness of the objective function under the condition of meeting the optimization end condition;

[0066] The second parameter solving unit is used to solve the dominant parameter to be identified of each equivalent model by using the Grey Wolf algorithm based on the objective function F d and the constraint conditions, so as to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

[0067] The present invention provides a method and system for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources. In the method, multiple types of reactive power resources are classified according to the operating characteristics of different reactive power resources; the sensitivity values of each reactive power resource equivalent model are determined, and the dominant parameter that is most sensitive to voltage change among all controllable parameters of the equivalent model is selected. Taking the minimum of the comprehensive deviation of voltage fluctuation and reactive power output as the objective function, the dominant parameter of the equivalent model is solved to form an equivalent model of multiple types of reactive power resources. The present invention can improve the equivalent modeling accuracy of multiple types of reactive power resources and provide technical support for reactive power regulation and voltage control of power systems. Brief Description of the Drawings

[0068] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0069] Figure 1 is a schematic flow chart of a method for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources provided by an embodiment of the present invention;

[0070] Figure 2 is a schematic diagram of the system topology of EPRI-36 nodes in PSASP provided by an embodiment of the present invention;

[0071] Figure 3 is an operating curve diagram of each model under a stable operating condition scenario provided by an embodiment of the present invention;

[0072] Figure 4 is an operating curve diagram of each model under a critically stable operating condition scenario provided by an embodiment of the present invention;

[0073] Figure 5 These are the operation curve graphs of each model provided by the embodiments of the present invention under the unstable operating condition scenario;

[0074] Figure 6 This is a schematic structural diagram of an equivalent model construction system considering the operation characteristics of multiple types of reactive power resources provided by the embodiments of the present invention. Detailed implementation manners

[0075] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0076] Embodiment 1

[0077] As Figure 1 shown, the embodiments of the present invention provide an equivalent model construction method considering the operation characteristics of multiple types of reactive power resources, including:

[0078] Step 101: Classify multiple reactive power sources and consuming devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consuming device.

[0079] In this step, the reactive power resources are classified according to the reactive power characteristics, dynamic response characteristics, and control methods to ensure the reasonable modeling of different types of reactive power resources.

[0080] Since the physical structures and working principles of resources such as synchronous generators, water turbines, and synchronous condensers all rely on rotational motion to achieve energy conversion and output reactive power control, such rotational output units are classified as rotational reactive power resources.

[0081] Devices such as photovoltaic power stations, wind farms, static var compensators, and static synchronous compensators all achieve grid connection operation through converters or inverters, and can achieve functions such as fast dynamic response, bidirectional reactive power regulation, independent control ability, and precise regulation. Such power electronic devices are classified as power electronic reactive power resources.

[0082] The active and reactive powers consumed by conventional loads such as electric heating equipment, reactors, and old-fashioned lighting equipment have an algebraic equation relationship with their port voltages and frequencies in general, and their operation characteristics show a linear relationship with the consumed reactive power. They can be classified as static reactive power loads.

[0083] Equipment such as induction motors, large pumps, and compressors has time - dynamic characteristics, that is, the reactive power changes with the operating state, and is greatly affected by voltage. It can be classified as dynamic reactive load.

[0084] The reactive power consumption of devices such as variable - frequency air conditioners, electric - vehicle charging stations, and computer servers has controllability and dynamics. The reactive power they consume or provide depends on the control strategy, usually generates harmonics, and can quickly respond to voltage changes. It can be classified as power - electronic - type reactive load.

[0085] Select the most representative equipment among various types of reactive power resources as the equivalent model of the corresponding reactive power resource. Among them, the equivalent model of rotational reactive power resource is the synchronous - generator model, the equivalent model of power - electronic - type reactive power resource is the single - stage photovoltaic model, the equivalent model of static reactive load is the ZIP load model, the equivalent model of dynamic reactive load is the induction - motor model, and the equivalent model of power - electronic - type load is the energy - storage model.

[0086] Step 102: In the typical scenarios of the target power - grid voltage fluctuation, determine the sensitivity values of each equivalent model to determine the dominant parameters in each equivalent model that are most sensitive to voltage changes.

[0087] In this step, set the typical scenarios of voltage fluctuation under the target power grid, including voltage - stable scenario, voltage - critically - stable scenario, and voltage - unstable scenario.

[0088] The reactive power generated or consumed by various types of reactive power resources has the greatest impact on the external characteristics of the constructed equivalent model, and it can be equivalent to the parameters of the model during simulation settings. The relationships between the reactive - power output or consumption amount and the capacity of the five reactive - power resource equivalent - model ports in Step 101 are as follows, and they are all controllable parameters:

[0089]

[0090] Among them, α is the ratio of the reactive power generated by the power - electronic - type reactive power resource under the target bus to the corresponding reactive - power capacity; Q PV_B is the reactive power generated by the power - electronic - type reactive power resource; S PV_B is the capacity of the power - electronic - type reactive power resource under the target bus; β is the ratio of the reactive power generated by the rotational reactive power resource under the target bus to the corresponding reactive - power capacity; Q SG_B is the reactive power generated by the rotational reactive power resource; S SG_B is the capacity of the rotational reactive power resource under the target bus; γ is the ratio of the reactive power consumed by the static reactive load under the target bus to the corresponding reactive - power capacity; Q Z_B is the reactive power consumed by the static reactive load; Q bus is the reactive power consumed by the equivalent impedance under the target bus; S Z_Bis the capacity of the dynamic reactive load under the target bus; λ is the ratio of the reactive power consumed by the dynamic reactive load under the target bus to the corresponding reactive capacity; Q BESS_B is the reactive power consumed by the dynamic reactive load; S BESS_B is the capacity of the dynamic reactive load under the target bus; η is the ratio of the reactive power consumed by the power electronic load under the target bus to the corresponding reactive capacity; Q IM_B is the reactive power consumed by the power electronic load; S IM_B is the capacity of the power electronic load under the target bus.

[0091] Calculate the trajectory sensitivity of each parameter in each equivalent model according to the following formula:

[0092]

[0093] where is the derivative of the voltage change at the target node i with respect to the parameter j; SE j is the trajectory sensitivity corresponding to the parameter j; O i is the voltage response trajectory of the target node i in the target equivalent model; θ j is the normalized value of the parameter j; Δθ j is the change amount of the parameter j; θ m is the normalized value of the parameter m; m is the parameter space dimension; t represents the t-th sampling point.

[0094] Take the parameters corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as the leading parameters.

[0095] Exemplarily, sort the sensitivities corresponding to each parameter, and according to actual requirements, screen out the leading parameters that are more sensitive to voltage changes among all controllable parameters of the equivalent model, calculate the trajectory sensitivities of all adjustable parameters, and its set SEN is as follows:

[0096] SEN = {SE1, SE2, …, SE M}.

[0097] where SE M is the trajectory sensitivity of the M-th adjustable parameter of the reactive power resource equivalent model.

[0098] Sort all the elements in the set SEN in descending order, select the adjustable parameters corresponding to the top 10% of the elements as the leading parameters of the reactive power resource equivalent model for identification and solution, and assign values to the remaining adjustable parameters using the capacity weighting method.

[0099] Step 103, taking the minimum comprehensive deviation of voltage fluctuation and reactive power output as the optimization goal, solve the leading parameters of each equivalent model to obtain an equivalent model reflecting the operating characteristics of various types of reactive power resources.

[0100] In this step, to accurately identify the dominant parameters of the equivalent model of the selected reactive power resources, an optimization model needs to be constructed. Exemplarily, an objective function F is constructed with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0101]

[0102] where L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node.

[0103] The constraint condition can be that the parameters to be identified in the equivalent model of reactive power resources are within the adjustable range and the number of iterations of the optimization algorithm reaches the set upper limit of the number of iterations. The constructed constraint condition is:

[0104]

[0105] where x min is the lower limit of the adjustable range of the parameter to be identified as the dominant parameter; x max is the upper limit of the adjustable range of the parameter to be identified as the dominant parameter; n is the current number of iterations of the optimization algorithm; N is the upper limit of the current number of iterations of the optimization algorithm.

[0106] The constraint condition can also be that the parameters to be identified in the equivalent model of reactive power resources are within the adjustable range and the fitness corresponding to the objective function meets the set minimum fitness requirement during the iterative optimization process. The constructed constraint condition is:

[0107]

[0108] Based on the objective function F dAnd constraint conditions, the grey wolf algorithm is used to solve the to-be-identified dominant parameters of each equivalent model, and an equivalent model reflecting the operating characteristics of multiple types of reactive power resources is obtained. That is, according to the above-mentioned optimization model with the minimum deviation of the voltage and reactive power output of the target node as the objective function, and combined with the corresponding constraint conditions, an improved grey wolf algorithm that is more conducive to multi-objective solution is used to solve the to-be-identified parameters of the reactive power resource equivalent model. Finally, an equivalent model of multiple types of reactive power resources with fewer parameters to be identified, easier solution, faster modeling, and accurate simulation is obtained.

[0109] Taking the EPRI-36 bus case in PSASP as the test system for the identification algorithm, the system topology is as Figure 2 shown. The test system consists of AC power grids with two voltage levels of 500 kV and 220 kV. Taking the reactive power resource models under the 5th bus as the analysis object, the capacities of the rotating reactive power resources, power electronic reactive power resources, static reactive power loads, power electronic reactive power loads, and dynamic reactive power loads are 220 MVA, 128 MVA, 34 MVA, 124 MVA, and 136 MVA respectively.

[0110] The parameters of other reactive power resources in the test system remain unchanged, and only all adjustable parameters of the 5th reactive power resource model are changed during the simulation. A three-phase short circuit, an A-phase single-phase short circuit, and a single-phase open circuit fault are set at the middle section of the AC line between BUS4 and BUS5, 20% of the AC line near BUS14 between BUS10 and BUS14, and the middle section of the AC line between BUS9 and BUS13 in the system respectively.

[0111] Based on the method for identifying the dominant parameters of the reactive power resource equivalent model proposed in this embodiment, the sensitivities of more than 200 adjustable parameters of the comprehensive load model under the 5th bus in the system are calculated under three fault modes. When the model parameters are all taken as the midpoints of the intervals. Taking Δθ as 0.001 (θ is the parameter normalized according to the adjustment range), the numerical sensitivities of each parameter in the parameter space are calculated one by one in this scenario. The parameters that have an obvious impact on the external characteristics of the model in the typical fault scenario include 12 steady-state parameters and 28 dynamic parameters. Observing the calculation results, the reactive power ratio parameters α, β, γ, λ, and η are nearly 10 times higher than other parameters, that is, the influence of the proportion of different types of loads on the comprehensive model is much greater than the internal parameters of the model.

[0112] Similarly, taking these three typical faults as the identification scenarios to identify the typical values of all identifiable parameters, the improved grey wolf optimization algorithm mentioned above is used for parameter identification. The improved grey wolf optimization algorithm has good identification effects on most parameters of multi-type reactive power resource models. Especially for the reactive power components α, β, γ, λ, and η with much higher sensitivities than other parameters, the relative error is less than 3%. This is because they have a greater impact on the dynamic response curve of the model. During the exploration process of the algorithm in the parameter space, the closer it gets to the actual values in these three dimensions, the more the objective function (fitness) is improved.

[0113] To illustrate the improvement effect of the method proposed in this embodiment on the parameter identification ability of the reactive power resource equivalent model, the idea of capacity weighting is used to establish the corresponding reactive power equivalent model, as Figure 3 , Figure 4 and Figure 5 shown, which shows the operation curves of the method proposed in this embodiment (i.e., the equivalent model construction method considering the operation characteristics of multi-type reactive power resources), the capacity weighting method, and the detailed model under various typical working conditions (including stable conditions, critical stable conditions, and unstable conditions).

[0114] Comparing the identification effects of the improved algorithm under three fault scenarios, compared with the capacity weighting method, the reactive power resource equivalent model constructed based on the method proposed in this embodiment is closer to the detailed model in the steady-state and transient processes under the three typical scenarios. Especially, its transient process is more in line with the response curve of the detailed model. This is because after adding mutations to individuals with lower fitness, the exploration efficiency of the population is improved. While ensuring the identification accuracy of high-sensitivity parameters, further breakthroughs can be made in the parameter dimensions with low sensitivities.

[0115] In summary, this embodiment provides an equivalent model construction method considering the operation characteristics of multi-type reactive power resources. First, analyze the working principles of various resources, and classify multi-type reactive power resources according to the operation characteristics of different reactive power resources. Second, establish typical scenarios of voltage fluctuations, calculate the sensitivity values of each reactive power resource equivalent model based on the analysis method of trajectory sensitivity, screen out the dominant parameters that are most sensitive to voltage changes among all controllable parameters of the equivalent model, and clarify them as the parameters to be identified during the construction of the reactive power resource equivalent model. Finally, taking the minimum comprehensive deviation of voltage fluctuations and reactive power output as the objective function, use the improved grey wolf algorithm to solve the dominant parameters of the equivalent model to form an equivalent model of multi-type reactive power resources. This embodiment can improve the equivalent modeling accuracy of multi-type reactive power resources and provide technical support for reactive power regulation and voltage control of power systems.

[0116] Embodiment 2

[0117] Based on the same inventive concept as Embodiment 1, this embodiment also provides an equivalent model construction system considering the operating characteristics of multiple types of reactive power resources. Since the principle of problem-solving of this system is similar to the equivalent model construction method considering the operating characteristics of multiple types of reactive power resources described above, the implementation of this system can refer to the implementation of the equivalent model construction method considering the operating characteristics of multiple types of reactive power resources.

[0118] As Figure 6 shown, the equivalent model construction system considering the operating characteristics of multiple types of reactive power resources includes:

[0119] The first determination module 10 is configured to classify multiple reactive power sources and consuming devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consuming device.

[0120] The second determination module 20 is configured to determine the sensitivity value of each equivalent model under typical scenarios of voltage fluctuations in the target power grid to determine the dominant parameter that is most sensitive to voltage changes in each equivalent model.

[0121] The parameter solution module 30 is configured to solve the dominant parameters of each equivalent model with the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization goal to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

[0122] Exemplarily, the first determination module includes:

[0123] The first determination unit is configured to use the output unit that relies on rotational motion to achieve energy conversion and output reactive power control as the rotational reactive power resource; the equivalent model of the rotational reactive power resource is a synchronous generator model.

[0124] The second determination unit is configured to use the power electronic devices that are connected to the grid through converters or inverters as the power electronic reactive power resources; the equivalent model of the power electronic reactive power resources is a single-stage photovoltaic model.

[0125] The third determination unit is configured to use the load whose consumed active or reactive power has an algebraic equation relationship with the port voltage or frequency and whose operating characteristics are linearly related to the consumed reactive power as the static reactive load; the equivalent model of the static reactive load is a ZIP load model.

[0126] The fourth determination unit is configured to use the load whose reactive power changes with the operating state and is affected by voltage as the dynamic reactive load; the equivalent model of the dynamic reactive load is an induction motor model.

[0127] The fifth determination unit is configured to use the load whose consumed or provided reactive power depends on the control strategy and responds to voltage changes as the power electronic reactive load; the equivalent model of the power electronic reactive load is an energy storage model.

[0128] Exemplarily, the second determination module includes:

[0129] A first calculation unit, configured to calculate the trajectory sensitivity of each parameter in each equivalent model according to the following formula:

[0130]

[0131] where SE j is the trajectory sensitivity corresponding to parameter j; O i is the voltage response trajectory of the target node i in the target equivalent model; θ j is the normalized value of parameter j; Δθ j is the change amount of parameter j; θ m is the normalized value of parameter m; m is the parameter space dimension; t represents the t-th sampling point.

[0132] A sixth determination unit, configured to use the parameter corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as the dominant parameter.

[0133] Exemplarily, the parameter solving module includes:

[0134] A first construction unit, configured to construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0135]

[0136] where L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the reactive power resource equivalent model; U sim is the voltage value of the target node under the reactive power resource detailed model; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the reactive power resource equivalent model; Q sim is the reactive power output value of the reactive power resource detailed model; Q N is the rated reactive power capacity of the target node.

[0137] A second construction unit, configured to construct a constraint condition:

[0138]

[0139] where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x maxis the upper limit of the adjustable range of the dominant parameter to be identified; n is the current iteration number of the optimization algorithm; N is the upper limit of the current iteration number of the optimization algorithm.

[0140] The first parameter solving unit is used to solve the dominant parameter to be identified of each equivalent model based on the objective function F d and the constraint conditions by using the grey wolf algorithm, so as to obtain an equivalent model reflecting the operation characteristics of multiple types of reactive power resources.

[0141] Exemplarily, the parameter solving module includes:

[0142] The second construction unit is used to construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d :

[0143]

[0144] where L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node.

[0145] The third construction unit is used to construct constraint conditions:

[0146]

[0147] where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; F an is the fitness of the objective function corresponding to the n-th optimization iteration; F amin is the minimum value of the fitness of the objective function under the condition of meeting the optimization end condition;

[0148] The second parameter solving unit is used to solve the dominant parameter to be identified of each equivalent model based on the objective function F d and the constraint conditions by using the grey wolf algorithm, so as to obtain an equivalent model reflecting the operation characteristics of multiple types of reactive power resources.

[0149] For the more specific working processes of the above-mentioned modules, reference can be made to the corresponding content disclosed in Embodiment 1, which will not be elaborated here.

[0150] Example 3

[0151] This embodiment provides a computer device, including a processor and a memory. When the processor executes the computer program stored in the memory, the steps of the equivalent model construction method considering the operating characteristics of multiple types of reactive power resources described in Embodiment 1 are implemented.

[0152] For a more specific process of the above method, reference can be made to the corresponding content disclosed in Embodiment 1, and details will not be elaborated here.

[0153] Example 4

[0154] This embodiment provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the steps of the equivalent model construction method considering the operating characteristics of multiple types of reactive power resources described in Embodiment 1 are implemented.

[0155] For a more specific process of the above method, reference can be made to the corresponding content disclosed in Embodiment 1, and details will not be elaborated here.

[0156] Example 5

[0157] This embodiment provides a computer program product, including computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the steps of the equivalent model construction method considering the operating characteristics of multiple types of reactive power resources described in Embodiment 1 are implemented.

[0158] For a more specific process of the above method, reference can be made to the corresponding content disclosed in Embodiment 1, and details will not be elaborated here.

[0159] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the systems, devices, storage media, and computer program products disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0160] Those skilled in the art can clearly understand that the technologies in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0161] In some embodiments, the computer-executable instructions may be in the form of a program, software, a software module, a script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as a stand-alone program or being deployed as a module, a component, a subroutine, or other unit suitable for use in a computing environment.

[0162] As an example, the computer-executable instructions may or may not correspond to a file in a file system, may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, stored in a single file dedicated to the program being discussed, or, stored in multiple cooperating files (for example, files that store one or more modules, subroutines, or portions of code).

[0163] As an example, the computer-executable instructions may be deployed to execute on one electronic device, or on multiple electronic devices located at one location, or, on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0164] The present invention has been described in detail above in conjunction with specific embodiments and exemplary instances, but these descriptions should not be construed as limiting the present invention. Those skilled in the art understand that, without departing from the spirit and scope of the present invention, various equivalent substitutions, modifications, or improvements can be made to the technical solutions of the present invention and their implementation manners, and all of these fall within the scope of the present invention. The protection scope of the present invention is subject to the appended claims.

Claims

1. A method for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources, characterized in that Including: Classify multiple reactive power sources and consumption devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consumption device; Under typical scenarios of voltage fluctuations in the target power grid, determine the sensitivity values of each equivalent model to determine the dominant parameters in each equivalent model that are most sensitive to voltage changes; Taking the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization goal, solve the dominant parameters of each equivalent model to obtain an equivalent model that reflects the operating characteristics of multiple types of reactive power resources.

2. The equivalent model construction method according to claim 1, characterized in that, The classifying multiple reactive power sources and consumption devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consumption device includes: Regard the output units that rely on rotational motion to achieve energy conversion and output reactive power control as rotational reactive power resources; the equivalent model of the rotational reactive power resources is a synchronous generator model; Regard the power electronic devices that are connected to the grid through converters or inverters as power electronic reactive power resources; the equivalent model of the power electronic reactive power resources is a single-stage photovoltaic model; Regard the load whose consumed active or reactive power has an algebraic equation relationship with the port voltage or frequency and whose operating characteristics are linearly related to the consumed reactive power as a static reactive load; the equivalent model of the static reactive load is a ZIP load model; Regard the load whose reactive power changes with the operating state and is affected by voltage as a dynamic reactive load; the equivalent model of the dynamic reactive load is an induction motor model; Regard the load whose consumed or provided reactive power depends on the control strategy and responds to voltage changes as a power electronic reactive load; the equivalent model of the power electronic reactive load is an energy storage model.

3. The equivalent model construction method according to claim 1, wherein The determining the sensitivity values of each equivalent model under typical scenarios of voltage fluctuations in the target power grid to determine the dominant parameters in each equivalent model that are most sensitive to voltage changes includes: Calculate the trajectory sensitivity of each parameter in each equivalent model according to the following formula: Among them, SE j is the trajectory sensitivity corresponding to parameter j; O i is the voltage response trajectory of target node i in the target equivalent model; θ j is the normalized value of parameter j; Δθ j is the change amount of parameter j; θ m is the normalized value of parameter m; m is the parameter space dimension; t represents the t-th sampling point; Regard the parameters corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as the dominant parameters.

4. The equivalent model construction method according to claim 1, characterized in that The taking the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization goal, solving the dominant parameters of each equivalent model to obtain an equivalent model that reflects the operating characteristics of multiple types of reactive power resources includes: Construct an objective function F that minimizes the voltage fluctuation and reactive power output deviation of the target node d : Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node; Construct constraint conditions: where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; n is the current iteration number of the optimization algorithm; N is the upper limit of the current iteration number of the optimization algorithm; Based on the objective function F d and the constraint conditions, the gray wolf algorithm is used to solve the dominant parameters to be identified for each equivalent model, and an equivalent model reflecting the operating characteristics of multiple types of reactive power resources is obtained.

5. The equivalent model construction method according to claim 1, characterized in that The taking the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization goal, solving the dominant parameters of each equivalent model to obtain an equivalent model that reflects the operating characteristics of multiple types of reactive power resources includes: Construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d : Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node; Construct constraint conditions: where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; F an is the fitness of the objective function corresponding to the nth optimization iteration; F amin is the minimum value of the fitness of the objective function that satisfies the optimization termination condition; Based on the objective function F d and the constraint conditions, the gray wolf algorithm is used to solve the dominant parameters to be identified for each equivalent model, and an equivalent model reflecting the operating characteristics of multiple types of reactive power resources is obtained.

6. A system for constructing an equivalent model considering the operating characteristics of multiple types of reactive power resources, characterized in that Including: The first determination module is used to classify multiple reactive power sources and consumption devices in the target power grid to determine the equivalent model corresponding to each type of reactive power source or consumption device; The second determination module is used to determine the sensitivity values of each equivalent model under typical scenarios of voltage fluctuations in the target power grid to determine the dominant parameters in each equivalent model that are most sensitive to voltage changes; The parameter solving module is used to take the minimum comprehensive deviation of voltage fluctuations and reactive power output as the optimization goal, solve the dominant parameters of each equivalent model to obtain an equivalent model that reflects the operating characteristics of multiple types of reactive power resources.

7. The equivalent model construction system according to claim 6, characterized in that, The first determination module includes: A first determination unit, configured to use a generating unit that realizes energy conversion and outputs reactive power control depending on rotational motion as a rotational reactive power resource; an equivalent model of the rotational reactive power resource is a synchronous generator model; A second determination unit, configured to use a power electronic device that realizes grid connection operation through a converter or an inverter as a power electronic reactive power resource; an equivalent model of the power electronic reactive power resource is a single-stage photovoltaic model; A third determination unit, configured to use a load in which the consumed active or reactive power has an algebraic equation relationship with the port voltage or frequency, and the operating characteristics have a linear relationship with the consumed reactive power as a static reactive load; an equivalent model of the static reactive load is a ZIP load model; A fourth determination unit, configured to use a load in which the reactive power changes with the operating state and is affected by the voltage as a dynamic reactive load; an equivalent model of the dynamic reactive load is an induction motor model; A fifth determination unit, configured to use a load in which the consumed or provided reactive power depends on the control strategy and responds to voltage changes as a power electronic reactive load; an equivalent model of the power electronic reactive load is an energy storage model.

8. The equivalent model construction system according to claim 6, characterized in that The second determination module includes: A first calculation unit, configured to calculate the trajectory sensitivity of each parameter in each equivalent model according to the following formula: Among them, SE j is the trajectory sensitivity corresponding to parameter j; O i is the voltage response trajectory of target node i in the target equivalent model; θ j is the normalized value of parameter j; Δθ j is the change in parameter j; θ m is the normalized value of parameter m; m is the parameter space dimension; t represents the t-th sampling point; A sixth determination unit, configured to use the parameter corresponding to the trajectory sensitivity exceeding the trajectory sensitivity threshold in each equivalent model as a dominant parameter.

9. The equivalent model construction system according to claim 6, wherein The parameter solving module includes: The first construction unit is used to construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d : Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node; A second construction unit, configured to construct a constraint condition: where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; n is the current iteration number of the optimization algorithm; N is the upper limit of the current iteration number of the optimization algorithm; The first parameter solving unit is used to solve the to-be-identified dominant parameters of each equivalent model based on the objective function F d and the constraint conditions by using the grey wolf algorithm, so as to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

10. The equivalent model construction system according to claim 6, wherein The parameter solving module includes: The second construction unit is used to construct an objective function F with the minimum voltage fluctuation and reactive power output deviation of the target node d : Among them, L is the total number of typical scenarios set for the target power grid; L' represents the L'-th typical scenario; ρ1 is the first preset coefficient; U eq is the voltage value of the target node under the equivalent model of reactive power resources; U sim is the voltage value of the target node under the detailed model of reactive power resources; U N is the rated voltage value of the target node; ρ2 is the second preset coefficient; Q eq is the reactive power output value of the equivalent model of reactive power resources; Q sim is the reactive power output value of the detailed model of reactive power resources; Q N is the rated reactive power capacity of the target node; A third construction unit, configured to construct a constraint condition: where x min is the lower limit of the adjustable range of the dominant parameter to be identified; x max is the upper limit of the adjustable range of the dominant parameter to be identified; F an is the fitness of the objective function corresponding to the nth optimization iteration; F amin is the minimum value of the fitness of the objective function that satisfies the optimization termination condition; A second parameter solving unit, which is used to solve the to-be-identified dominant parameters of each equivalent model based on the objective function F d and the constraint conditions by using the grey wolf algorithm, so as to obtain an equivalent model reflecting the operating characteristics of multiple types of reactive power resources.

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