A grid reactive power and voltage control method considering source-load prediction error and sudden change

Abstract

The present invention proposes a grid reactive power and voltage control method that considers source-load prediction errors and sudden changes. First, grid data is initialized and active power, reactive power, active power loss, and capacity constraints for inverter-based distributed power sources and soft switches are constructed. Second, a coordinated optimization objective function for conventional voltage regulation equipment, inverter-based distributed power sources, and soft switches in the day-ahead scheduling phase is constructed. Combined with the constraints, a stochastic analysis based on k-means scenario reduction is used to determine the output configuration. Based on the aforementioned optimization results and prediction deviations, a rolling optimization control objective function based on model predictive control (MPC) is constructed for the intraday control phase to determine the output scheme for the inverter-based distributed power sources and soft switches. Finally, based on the aforementioned optimization scheme and power generation sudden changes, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization for the inverter-based distributed power sources and soft switches in the real-time control phase to alleviate voltage fluctuations caused by factors such as the intermittent nature of distributed power sources.

Description

Technical Field

[0001] The present invention relates to the field of reactive power voltage control of regional power grids, and in particular to a method for controlling reactive power voltage of power grids taking into account source-load prediction errors and mutations. Technical Background

[0002] Renewable energy is being connected to regional power grids in large quantities due to environmental concerns, technological innovation, and new policies. However, this high penetration can lead to low grid voltage and increased power demand. Conventional voltage-regulating devices, such as voltage regulators, shunt capacitor banks, and on-load tap-changing transformers (OLTCs), struggle to quickly respond to sudden voltage fluctuations caused by the intermittent nature of renewable energy. Furthermore, frequent operation shortens the lifespan of the devices themselves.

[0003] Distributed power sources and soft-switching devices based on inverter control have attracted much attention due to their flexibility. Inverter-based distributed power sources offer many advantages, including loss minimization, reactive power support, and voltage regulation. However, existing inverter-based distributed power devices increase system losses when used for voltage regulation. Soft-switching devices are often used to reduce losses and balance feeder loads, but the voltage regulation effect is poor. Power electronics-based and conventional voltage regulators coexist in regional power grids, but the combined impact of these different devices on the energy-saving operation of regional power grids has not been studied. Conventional voltage regulators cannot be operated frequently due to their physical limitations. Currently, there are no reactive voltage control methods for power grids that address source-load prediction errors and sudden changes. Therefore, a reactive voltage control method for power grids that considers source-load prediction errors and sudden changes can be proposed to eliminate the risks associated with high-penetration renewable energy generation. Summary of the Invention

[0004] The present invention proposes a grid reactive power voltage control method considering source-load prediction error and sudden change, which is characterized by comprising the following steps:

[0005] Step 1: Input initialization data such as regional power grid line node branch parameters, exponential load power model, polynomial load power model, on-load tap changer transformer tap settings, and shunt capacitor bank capacity settings.

[0006] Step 2: Construct active power constraints, reactive power constraints, active power loss conditions of the inverter-based distributed power supply, and active power constraints of the soft switch, reactive power constraints of the soft switch, and capacity constraints of the soft switch;

[0007] Step 3: Enter the day-ahead scheduling phase, construct the coordinated optimization objective function of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switches, as well as the regional power grid flow constraints and bus voltage amplitude limit constraints. Combined with the constraints in step 2, the output configuration of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switches is given by random analysis based on k-means scenario reduction and discrete continuous optimization algorithm.

[0008] Step 4: Enter the intraday control stage. According to the day-ahead scheduling optimization results and forecast deviation, a rolling optimization control objective function based on model predictive control is constructed. Combined with the constraints in steps 2 and 3, the output scheme of the inverter-based distributed power supply equipment and soft switching device in this stage is determined.

[0009] Step 5: Enter the real-time control phase. Based on the results of the intraday control phase and the sudden changes in power generation and load, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization of inverter-based distributed power devices and soft switches to alleviate the voltage fluctuation problems caused by the intermittent behavior of solar radiation and other meteorological factors.

[0010] Preferably, the exponential load power model in step 1 is defined as:

[0011]

[0012] Where: V i t is the exponential load active power, reactive power, and node voltage of node i in the tth period; V i t,nom is the active power, reactive power and node voltage absorbed by the exponential load of node i in the tth period under the steady state before the disturbance; is determined by the load type at node i.

[0013] The polynomial load power model described in step 1 is defined as:

[0014]

[0015] Where: Zi 、 Ii 、 Pi is the proportion of constant impedance, constant current, and constant power in the total load at node i; is the polynomial load active and reactive power of node i in the tth period; is the active and reactive power absorbed by the polynomial load of node i in the tth period in the steady state before the disturbance.

[0016] The definition of the on-load tap changer setting in step 1 is:

[0017]

[0018] Where: tap t ∈{tap min,t ,…,-1,0,1,…tap max,t}, is the tap position of the on-load tap-changing transformer in the tth period;

[0019] The definition of the parallel capacitor bank setup described in step 1 is:

[0020]

[0021] Where: is the switching step of the parallel capacitor bank on node i in the tth period; is the rated value of the capacitor bank at node i in the tth period; is the capacitor bank step change at node i; Ω cap is the collection of node capacitor banks.

[0022] Preferably, the active power constraint condition of the inverter-based distributed power supply in step 2 is:

[0023]

[0024] Where: is the active power of the inverter-based distributed generation on node i in the tth period; is the maximum active power limit of the inverter-based distributed generation at node i in the tth period.

[0025]

[0026] Where: is the real-time active power of the inverter-based distributed generation on node i in the tth period; is the active power loss of the inverter-based distributed generation at node i in the tth period.

[0027] The reactive power constraint condition of the inverter-based distributed power supply in step 2 is:

[0028]

[0029] Where: is the reactive power of the inverter-based distributed generation on node i in the tth period; is the maximum reactive power limit of the inverter-based distributed generation at node i in the tth period; is the maximum apparent power limit of the inverter-based distributed generation at node i in the tth period.

[0030] The active power loss condition of the inverter-based distributed power supply in step 2 is:

[0031]

[0032] Where: η inv is the loss coefficient of the inverter-based distributed generation at node i.

[0033] The active power constraint of the soft switch in step 2 is:

[0034]

[0035] Where: is the active power and active loss of the soft switch on node i in the tth period; is the active power and active loss of the soft switch on node j in the tth period.

[0036]

[0037] Where: A i,s 、A j,s is the loss coefficient of the soft switch at nodes i and j; is the reactive power of the soft switches on nodes i and j in the tth period.

[0038] The reactive power constraint of the soft switch in step 2 is:

[0039]

[0040] Where: is the minimum and maximum limit of reactive power of the soft switch at node i; are the minimum and maximum limits of reactive power of the soft switch at node j.

[0041] The capacity constraint of the soft switch in step 2 is:

[0042]

[0043] Where: S ij,s is the apparent power of the soft switch at node i.

[0044] Preferably, the coordinated optimization objective function of the conventional voltage regulating equipment, the inverter-based distributed power supply equipment, and the soft switch in step 3 is composed of two parts: the power consumption reduced by the CVR operation and the network loss in the regional power grid:

[0045]

[0046] Where: The first term represents the power consumption reduced by CVR operation, ω1 is the power consumption coefficient, Ω Nd is the total number of regional power grid nodes, is the node voltage with CVR operation in the tth period; the second term is the network loss in the regional power grid, is the active power loss of connected node i in the tth period.

[0047] The regional power grid flow constraints in step 3 are:

[0048]

[0049] Where: is the active power of the regional power grid in the tth period; N d is the total number of regional power grid nodes; is the voltage of node j in the tth period; is the branch conductance between nodes ij in the tth period, is the branch susceptance between nodes ij in the tth time period; is the phase angle of node i in the tth period; is the phase angle of node j in the tth time period.

[0050]

[0051] Where: Reactive power of the regional power grid in the tth period;

[0052] The bus voltage amplitude limit constraint conditions in step 3 are:

[0053] V min ≤V i t ≤V max

[0054] Where: V min 、V max are the minimum and maximum voltage limits of node i in the tth time period.

[0055] The purpose of the random analysis based on k-means scenario reduction in step 3 is to further adjust the coordinated optimization objective function. The coordinated optimization objective function is adjusted to:

[0056]

[0057] Where: N r To reduce the number of scenes; The probability of reducing the number of scenes is represented by .

[0058] Preferably, the objective function of the rolling optimization control based on model predictive control in step 4 is:

[0059]

[0060] Where: T p is the prediction range, t k For the time scale.

[0061] Preferably, step 5 enters the real-time control phase. Based on the results of the intraday control phase and the sudden changes in power generation / load, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization of the inverter-based distributed power supply equipment and soft switches to alleviate the voltage fluctuation caused by the intermittent behavior of solar radiation and other meteorological factors. The voltage and reactive power scheduling optimization function of the inverter-based distributed power supply equipment and soft switches is:

[0062]

[0063] Where: is the compensated reactive power of the inverter-based distributed power supply / soft switch at time t; is the voltage of node A1; is the voltage of node A2; is the voltage of node A3; is the voltage of node A4.

[0064] The advantage of the present invention is that the proposed grid reactive voltage control method that takes into account source-load prediction errors and mutations can effectively cope with sudden changes in power generation / load and prediction deviations, eliminate the risks brought about by high-penetration renewable energy power generation, and alleviate the problems of voltage fluctuations caused by the intermittent behavior of solar radiation and other meteorological factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 : is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] The specific embodiment of the present invention is a grid reactive voltage control method considering source-load prediction error and sudden change, such as Figure 1 As shown, the following steps are included:

[0068] Step 1: Input initialization data such as regional power grid line node branch parameters, exponential load power model, polynomial load power model, on-load tap changer transformer tap settings, and shunt capacitor bank capacity settings.

[0069] The exponential load power model described in step 1 is defined as:

[0070]

[0071] Where: V i t is the exponential load active power, reactive power, and node voltage of node i in the tth period; V i t,nom is the active power, reactive power and node voltage absorbed by the exponential load of node i in the tth period under the steady state before the disturbance; is determined by the load type at node i.

[0072] The polynomial load power model described in step 1 is defined as:

[0073]

[0074] Where: Zi 、 Ii 、 Pi is the proportion of constant impedance, constant current, and constant power in the total load at node i; is the polynomial load active and reactive power of node i in the tth period; is the active and reactive power absorbed by the polynomial load of node i in the tth period in the steady state before the disturbance.

[0075] The definition of the on-load tap changer setting in step 1 is:

[0076]

[0077] Where: tap t ∈{tap min,t ,…,-1,0,1,…tap max,t}, is the tap position of the on-load tap-changing transformer in the tth period;

[0078] The definition of the parallel capacitor bank setup described in step 1 is:

[0079]

[0080] Where: is the switching step of the parallel capacitor bank on node i in the tth period; is the rated value of the capacitor bank at node i in the tth period; is the capacitor bank step change at node i; Ω cap is the collection of node capacitor banks.

[0081] Step 2: Construct active power constraints, reactive power constraints, active power loss conditions of the inverter-based distributed power supply, and active power constraints of the soft switch, reactive power constraints of the soft switch, and capacity constraints of the soft switch;

[0082] The active power constraint condition of the inverter-based distributed power supply in step 2 is:

[0083]

[0084] Where: is the active power of the inverter-based distributed generation on node i in the tth period; is the maximum active power limit of the inverter-based distributed generation at node i in the tth period.

[0085]

[0086] Where: is the real-time active power of the inverter-based distributed generation on node i in the tth period; is the active power loss of the inverter-based distributed generation at node i in the tth period.

[0087] The reactive power constraint condition of the inverter-based distributed power supply in step 2 is:

[0088]

[0089] Where: is the reactive power of the inverter-based distributed generation on node i in the tth period; is the maximum reactive power limit of the inverter-based distributed generation at node i in the tth period; is the maximum apparent power limit of the inverter-based distributed generation at node i in the tth period.

[0090] The active power loss condition of the inverter-based distributed power supply in step 2 is:

[0091]

[0092] Where: η inv is the loss coefficient of the inverter-based distributed generation at node i.

[0093] The active power constraint of the soft switch in step 2 is:

[0094]

[0095] Where: is the active power and active loss of the soft switch on node i in the tth period; is the active power and active loss of the soft switch on node j in the tth period.

[0096]

[0097] Where: A i,s 、A j,s is the loss coefficient of the soft switch at nodes i and j; is the reactive power of the soft switches on nodes i and j in the tth period.

[0098] The reactive power constraint of the soft switch in step 2 is:

[0099]

[0100] Where: is the minimum and maximum limit of reactive power of the soft switch at node i; are the minimum and maximum limits of reactive power of the soft switch at node j.

[0101] The capacity constraint of the soft switch in step 2 is:

[0102]

[0103] Where: S ij,s is the apparent power of the soft switch at node i.

[0104] Step 3: Enter the day-ahead scheduling phase, construct the coordinated optimization objective function of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switches, as well as the regional power grid flow constraints and bus voltage amplitude limit constraints. Combined with the constraints in step 2, the output configuration of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switches is given by random analysis based on k-means scenario reduction and discrete continuous optimization algorithm.

[0105] The coordinated optimization objective function of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switching in step 3 consists of two parts: the power consumption reduced by CVR operation and the network loss in the regional power grid:

[0106]

[0107] Where: The first term represents the power consumption reduced by CVR operation, ω1 is the power consumption coefficient, Ω Nd is the total number of regional power grid nodes, is the node voltage with CVR operation in the tth period; the second term is the network loss in the regional power grid, is the active power loss of connected node i in the tth period.

[0108] The regional power grid flow constraints in step 3 are:

[0109]

[0110] Where: is the active power of the regional power grid in the tth period; N d is the total number of regional power grid nodes; is the voltage of node j in the tth period; is the branch conductance between nodes ij in the tth period, is the branch susceptance between nodes ij in the tth time period; is the phase angle of node i in the tth period; is the phase angle of node j in the tth time period.

[0111]

[0112] Where: Reactive power of the regional power grid in the tth period;

[0113] The bus voltage amplitude limit constraint conditions in step 3 are:

[0114]

[0115] Where: V min 、V max are the minimum and maximum voltage limits of node i in the tth time period.

[0116] The purpose of the random analysis based on k-means scenario reduction in step 3 is to further adjust the coordinated optimization objective function. The coordinated optimization objective function is adjusted to:

[0117]

[0118] Where: N r To reduce the number of scenes; The probability of reducing the number of scenes is represented by .

[0119] Step 4: Enter the intraday control stage. According to the day-ahead scheduling optimization results and forecast deviation, a rolling optimization control objective function based on model predictive control is constructed. Combined with the constraints in steps 2 and 3, the output scheme of the inverter-based distributed power supply equipment and soft switching device in this stage is determined.

[0120] The objective function of the rolling optimization control based on model predictive control in step 4 is:

[0121]

[0122] Where: T p is the prediction range, t k For the time scale.

[0123] Step 5: Enter the real-time control phase. Based on the results of the intraday control phase and the sudden changes in power generation and load, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization of inverter-based distributed power devices and soft switches to alleviate the voltage fluctuation problems caused by the intermittent behavior of solar radiation and other meteorological factors.

[0124] Step 5 enters the real-time control phase. Based on the results of the intraday control phase and the sudden changes in power generation / load, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization of the inverter-based distributed power supply equipment and soft switches to alleviate the voltage fluctuation caused by the intermittent behavior of solar radiation and other meteorological factors. The voltage and reactive power scheduling optimization function of the inverter-based distributed power supply equipment and soft switches is:

[0125]

[0126] Where: is the compensated reactive power of the inverter-based distributed power supply / soft switch at time t; is the voltage of node A1; is the voltage of node A2; is the voltage of node A3; is the voltage of node A4.

[0127] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0128] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A method for controlling reactive power and voltage of a power grid considering source-load prediction errors and sudden changes, characterized in that: The following steps are involved: Step 1: Input initialization data such as regional power grid line node branch parameters, exponential load power model, polynomial load power model, on-load tap changer transformer tap settings, and shunt capacitor bank capacity settings; Step 2: Construct active power constraints, reactive power constraints, active power loss conditions of the inverter-based distributed power supply, and active power constraints of the soft switch, reactive power constraints of the soft switch, and capacity constraints of the soft switch; Step 3: Enter the day-ahead scheduling phase, constructing the coordinated optimization objective function for conventional voltage regulation equipment, inverter-based distributed power generation equipment, and soft switches, as well as the regional grid power flow constraints and bus voltage amplitude limit constraints. Combining the constraints in step 2, the output configurations of conventional voltage regulation equipment, inverter-based distributed power generation equipment, and soft switches are determined using stochastic analysis based on k-means scenario reduction and a discrete-continuous optimization algorithm. Step 4: Enter the intraday control phase. Based on the day-ahead dispatch optimization results and forecast deviation, a rolling optimization control objective function based on model predictive control is constructed. Combined with the constraints in steps 2 and 3, the output scheme of the inverter-based distributed power supply equipment and soft switching devices in this phase is determined. Step 5: Entering the real-time control phase, based on the results of the intraday control phase and sudden changes in power generation and load, an autonomous control algorithm is used to determine the optimal voltage and reactive power scheduling of inverter-based distributed power devices and soft switches to alleviate voltage fluctuations caused by intermittent solar radiation and other meteorological factors. The exponential load power model described in step 1 is defined as: Where: is the exponential load active power, reactive power, and node voltage of node i in the tth period; is the active power, reactive power and node voltage absorbed by the exponential load of node i in the tth period under the steady state before the disturbance; is determined by the load type at node i; The polynomial load power model described in step 1 is defined as: Where: Z i , I i 、P i is the proportion of constant impedance, constant current, and constant power in the total load at node i; is the polynomial load active and reactive power of node i in the tth period; is the active and reactive power absorbed by the polynomial load of node i in the tth period under the steady-state condition before the disturbance; The definition of the on-load tap changer setting in step 1 is: Where: tap t ∈{tap min,t ,…,-1,0,1,…tap max,t }, is the tap position of the on-load tap-changing transformer in the tth period; The definition of the parallel capacitor bank setup described in step 1 is: Where: is the switching step of the parallel capacitor bank on node i in the tth period; is the rated value of the capacitor bank at node i in the tth period; is the capacitor bank step change at node i; Ω cap is the node capacitor bank set; The active power constraint of the inverter-based distributed power supply in step 2 is: Where: is the active power of the inverter-based distributed generation on node i in the tth period; is the maximum active power limit of the inverter-based distributed generation at node i in the tth period; Where: is the real-time active power of the inverter-based distributed generation on node i in the tth period; is the active power loss of the inverter-based distributed generation on node i in the tth period; The reactive power constraint condition of the inverter-based distributed power supply in step 2 is: Where: is the reactive power of the inverter-based distributed generation on node i in the tth period; is the maximum reactive power limit of the inverter-based distributed generation at node i in the tth period; is the maximum apparent power limit of the inverter-based distributed generation at node i in the tth period; The active power loss condition of the inverter-based distributed power supply in step 2 is: Where: η inv is the loss coefficient of the inverter-based distributed generation at node i; The active power constraint of the soft switch in step 2 is: Where: is the active power and active loss of the soft switch on node i in the tth period; is the active power and active loss of the soft switch on node j in the tth period; Where: A i,s 、A j,s is the loss coefficient of the soft switch at nodes i and j; is the reactive power of the soft switch on nodes i and j in the tth period; The reactive power constraint of the soft switch in step 2 is: Where: is the minimum and maximum limit of reactive power of the soft switch at node i; is the minimum and maximum limit of reactive power of the soft switch at node j; The capacity constraint of the soft switch in step 2 is: Where: S ij,s is the apparent power of the soft switch on node i; The coordinated optimization objective function of conventional voltage regulation equipment, inverter-based distributed power supply equipment, and soft switch in step 3 is composed of two parts: power consumption reduced by CVR operation and network loss in the regional power grid. composition: Where: The first term represents the power consumption reduced by CVR operation, ω1 is the power consumption coefficient, Ω Nd is the total number of regional power grid nodes, is the node voltage with CVR operation in the tth period; the second term is the network loss in the regional power grid, is the active power loss of the connected node i in the tth period; The regional power grid flow constraints in step 3 are: Where: is the active power of the regional power grid in the tth period; N d is the total number of regional power grid nodes; is the voltage of node j in the tth period; is the branch conductance between nodes ij in the tth period, is the branch susceptance between nodes ij in the tth time period; is the phase angle of node i in the tth period; is the phase angle of node j in the tth period; Where: Reactive power of the regional power grid in the tth period; The bus voltage amplitude limit constraint conditions in step 3 are: Where: V min 、V max are the minimum and maximum voltage limits of node i in the tth period; The purpose of the random analysis based on k-means scenario reduction in step 3 is to further adjust the coordinated optimization objective function. The coordinated optimization objective function is adjusted to: Where: N r To reduce the number of scenes; To reduce the number of scenarios, the probability is represented by; The objective function of the rolling optimization control based on model predictive control in step 4 is: Where: T p is the prediction range, t k is the time scale; Step 5 enters the real-time control phase. Based on the results of the intraday control phase and the sudden changes in power generation and load, an autonomous control algorithm is used to determine the voltage and reactive power scheduling optimization of the inverter-based distributed power supply equipment and soft switches to alleviate the voltage fluctuation caused by the intermittent behavior of solar radiation and other meteorological factors. The voltage and reactive power scheduling optimization function of the inverter-based distributed power supply equipment and soft switches is: Where: is the compensated reactive power of the inverter-based distributed power supply / soft switch at time t; is the voltage of node A1; is the voltage of node A2; is the voltage of node A3; is the voltage of node A4.

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