Power distribution network multi-time scale voltage control method considering source load uncertainty

Abstract

The invention discloses a power distribution network multi-time scale voltage control method considering source load uncertainty, and belongs to the field of power system voltage control. Establishing a corresponding power distribution network voltage day-ahead control model, a power distribution network voltage intra-day control model and a power distribution network voltage real-time control model for the typical power distribution network scene, and solving; according to a set value obtained through solving, reactive power compensation equipment adjustment in a day-ahead stage, reactive power output adjustment on a distributed power supply in an intra-day stage and real-time active power output adjustment on the distributed power supply are carried out, and response time differences of different voltage compensation equipment are fully considered; and the node voltage out-of-limit problem caused by the access of various uncertain source loads to the power distribution network can be effectively suppressed.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system voltage control, and in particular relates to a multi-time scale voltage control method for a distribution network taking source and load uncertainty into consideration. Background Art

[0002] Distributed power sources are highly flexible, environmentally friendly, and easy to control locally. They have become an important part of addressing climate change and ensuring energy security, and are also an important part of the new power system. In addition, due to the energy-saving and low-emission advantages of electric vehicles, the development of electric vehicles is favored by governments and consumers. If distributed power sources are used in conjunction with electric vehicles, carbon emissions can be further reduced; however, distributed power sources are random and uncertain in nature. The connection of electric vehicles to the power grid will increase the peak-to-valley difference in the power grid load and increase network losses, which is not conducive to the stable operation of the power grid.

[0003] As the penetration rate of power electronic equipment in distribution networks increases, the voltage fluctuation problem of distribution networks is becoming more and more serious. Both overvoltage and undervoltage will reduce energy efficiency, cause equipment surge and even damage user equipment. At present, the research on voltage control of distribution networks is mostly aimed at the voltage over-limit problem caused by a single distributed power source or electric vehicle, and there are relatively few studies that take into account the uncertainty on both sides of the source and load. Among them, researchers still rely on traditional reactive power control to maintain the voltage within a feasible range, adjust the on-load tap-changing transformer and the parallel capacitor group, and convert the reactive voltage control into an optimization problem to give the adjustment scheme of the reactive voltage compensation equipment. Some researchers also put the optimization of reactive voltage control and the interaction between production and consumers in a game model; or after considering the uncertainty of distributed power sources, a two-stage collaborative optimization scheme for reactive voltage control is proposed. However, the above studies did not consider the differences in the response speed of reactive voltage regulation equipment, and regarded reactive voltage control as a robust optimization problem, which had limited effect on the smoothing of voltage fluctuations caused by the real-time nature of uncertain sources and loads.

[0004] In addition, both distributed power sources and electric vehicles are connected to the grid using inverters, and inverters are extremely sensitive to changes in the voltage at the grid connection point. Changes in the output of distributed power sources and the charging and discharging characteristics of electric vehicles cause changes in the distribution network flow, and the grid connection point voltage changes accordingly, which will further affect the active and reactive power ranges of distributed power sources and electric vehicles. At the same time, distributed power sources are affected by power factors, and the reactive capacity of inverters is limited. It is difficult to completely solve the distribution network voltage problem by relying solely on distributed power sources. Therefore, some literature proposes to combine energy storage equipment to absorb surplus distributed power output or compensate for active power shortages, but the introduction of large-capacity energy storage equipment increases economic costs.

[0005] In summary, the introduction of distributed power sources and electric vehicles provides an opportunity to improve the power supply reliability of distribution networks. However, with the large-scale access of distributed power sources, electric vehicles and other power electronic equipment, the problem of voltage fluctuation in distribution networks is significant. Existing methods ignore the differences in the response time of voltage compensation equipment. How to further deal with the uncertainties on both the source and load sides, fully combine the regulation capabilities of distributed power sources, electric vehicles and reactive voltage compensation equipment, and achieve voltage optimization control in distribution station areas that is both economical and safe, has become an urgent problem to be solved. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention is implemented by the following technical solutions:

[0007] In a first aspect of the present invention, a multi-time-scale voltage control method for a distribution network considering source-load uncertainty is provided, comprising the following steps:

[0008] Determine the distribution network scenario s through distribution network scenario generation and distribution network scenario reduction;

[0009] Collect the day-ahead phase operation parameters, the intraday phase operation parameters and the real-time phase operation parameters of the distribution network;

[0010] According to the day-ahead operation parameters of the distribution network, a day-ahead voltage control model of the distribution network under scenario s is established, and the day-ahead voltage control model of the distribution network is solved to obtain the day-ahead set value of the reactive voltage compensation equipment;

[0011] According to the daily operation parameters of the distribution network, a distribution network voltage daily control model is established, and the distribution network voltage daily control model is solved to obtain the daily reactive power control reference value of the distributed generation;

[0012] A real-time control model of distribution network voltage is established according to the real-time operation parameters of the distribution network, and the real-time control model of distribution network voltage is solved to obtain the real-time active power control reference value of the distributed power source;

[0013] The reactive power compensation equipment is adjusted in the day-ahead stage according to the day-ahead set value of the reactive voltage compensation equipment, and the reactive power output of the distributed power source is adjusted in the intraday stage according to the intraday reactive power control reference value of the distributed power source; when the voltage at the distribution network node still exceeds the limit, the active power output of the distributed power source is adjusted according to the real-time active power control reference value of the distributed power source.

[0014] Furthermore, the distribution network scenario generation includes:

[0015] A source-load basic scenario set is established based on the wind speed state, light intensity state and electric vehicle state represented by the time series, and the source-load basic scenario set is sorted by Cholesky to obtain the distribution network scenario generation.

[0016] Furthermore, the distribution network scenario reduction includes:

[0017] Randomly select K scenarios in the distribution network scenario generation as cluster centers;

[0018] Traverse the remaining scenarios except K scenarios in the distribution network scenario generation, calculate the Euclidean distance from the remaining scenarios to the cluster center, determine the central scenario of the cluster center closest to the remaining scenarios, and divide the remaining scenarios into the classes represented by the central scenario with the closest Euclidean distance to generate new K cluster centers;

[0019] The average value group of the new K cluster centers is used as the new central scene for the next iteration until the central scene remains unchanged or the number of iterations reaches the upper limit, and the scene closest to the central scene among the K cluster centers is selected as the typical scene.

[0020] Furthermore, the scenario with the highest probability of occurrence in the typical scenarios is the distribution network scenario s.

[0021] Furthermore, the day-ahead control model of the distribution network voltage under scenario s includes a first objective function and a first constraint condition. The first objective function includes minimizing power loss and minimizing the number of switching times of reactive regulation equipment; the first constraint condition includes: network constraints, distributed power generation reactive capacity constraints, on-load tap-changing transformer constraints, shunt capacitor bank operation constraints, electric vehicle constraints, and electric vehicle charging station constraints.

[0022] Furthermore, the distribution network voltage intraday control model includes a second objective function and a second constraint condition. The second objective function is to minimize the voltage deviation of the distribution network voltage node; the second constraint condition includes: network constraint, distributed power source reactive capacity constraint and electric vehicle constraint.

[0023] Furthermore, the real-time control model of distribution network voltage includes a third objective function and a third constraint condition. The third objective function includes the function of minimizing the voltage deviation of the distribution network voltage nodes and minimizing the active power reduction of distributed power sources; the third constraint condition includes: distributed power source active power reduction constraint, network constraint, distributed power source reactive capacity constraint and electric vehicle constraint.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The present invention takes into account a variety of uncertain sources and loads, represents the source and load model based on a probability function, generates an original scenario set for the distribution network, and obtains representative scenarios by scenario reduction and screening, thereby reducing the uncertainty of the source and load in the distribution network.

[0026] 2. The present invention takes into account the differences in response time of different voltage regulation devices, combines multiple factors such as network loss, node voltage deviation, and distributed power output reduction, and constructs a multi-time scale voltage optimization model that can respond to source-load fluctuations in a timely manner.

[0027] 3. The present invention suppresses overvoltage in the distribution network by adjusting the real-time output of distributed energy and reactive power regulation equipment, thereby improving the existing technology of using energy storage and reactive voltage compensation equipment to achieve voltage optimization control with active and reactive power coordination, and reducing costs.

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A multi-time-scale voltage control method for a distribution network considering source-load uncertainty is provided in an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of an IEEE33 node distribution network provided by an embodiment of the present invention;

[0031] FIG3( a ) is a diagram showing the voltage distribution of feeder 1 in the initial IEEE 33-node distribution network before the method is adopted according to an embodiment of the present invention;

[0032] FIG3( b ) is a diagram showing the voltage distribution of feeder 1 in the IEEE 33-node distribution network after being controlled by the method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the scheme according to the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] The above and other technical contents, features and effects of the present invention are clearly presented in the following detailed description of the specific implementation modes in conjunction with the accompanying drawings. Through the description of the specific implementation modes, the technical means and effects adopted by the present invention to achieve the predetermined purpose can be more deeply and specifically understood. However, the attached drawings are only for reference and explanation purposes and are not used to limit the technical solutions of the present invention.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. In the absence of more restrictions, the elements defined by the statement "including one..." do not exclude the existence of other identical elements in the article or device including the elements.

[0036] like Figure 1 As shown, a multi-time-scale voltage control method for a distribution network considering source-load uncertainty is provided in an embodiment of the present invention, and the method comprises the following steps:

[0037] Step 1: Determine the distribution network scenario s through distribution network scenario generation and distribution network scenario reduction.

[0038] Step 1.1: Generate distribution network scenario:

[0039] Step 1.1.1: Establish a source-load basic scenario set based on the wind speed state, light intensity state and electric vehicle state represented by the time series.

[0040] Based on the wind speed state, light intensity state and electric vehicle state represented by time series, cumulative probability distribution functions are established respectively, and the cumulative probability distribution functions are divided into intervals. Sampling is performed according to arbitrary rules in each divided sub-interval to generate sample matrices of wind speed state, light intensity state and electric vehicle state, that is, the source-load basic scenario set.

[0041] Step 1.1.2: Perform Cholesky decomposition on the source-load basic scenario set to generate the distribution network scenario.

[0042] Randomly arrange and generate a sequential matrix of the same size as the sample matrix, calculate the rank correlation coefficient matrix and perform Cholesky decomposition to give the rank correlation coefficient matrix; perform Cholesky decomposition on the correlation coefficient matrix in the source-load basic scenario set, and transform the decomposed correlation coefficient matrix so that the transformed matrix is ​​similar to the rank correlation coefficient matrix of the sequential matrix. At this time, the matrix is ​​the required distribution network scenario generation.

[0043] Step 1.2: Distribution network scenario reduction:

[0044] K scenarios in the distribution network scenario generation are randomly selected as cluster centers.

[0045] Traverse the remaining scenarios except K scenarios in the distribution network scenario generation, calculate the Euclidean distance from the remaining scenarios to the cluster center, determine the central scenario of the cluster center closest to the remaining scenarios, and divide the remaining scenarios into the classes represented by the central scenario with the closest Euclidean distance to generate new K cluster centers;

[0046] The average value group of the new K cluster centers is used as the new central scene for the next iteration until the central scene remains unchanged or the number of iterations reaches the upper limit, and the scene closest to the central scene among the K cluster centers is selected as the typical scene.

[0047] Step 1.3: Select the scenario with the highest probability of occurrence in the typical scenarios as the distribution network scenario s.

[0048] Step 2: Collect the day-ahead phase operating parameters, the intraday phase operating parameters and the real-time phase operating parameters of the distribution network.

[0049] Among them, the distribution network in the day-ahead stage T 1 The operating parameters specifically include: all parameters in the first objective function of the distribution network voltage day-ahead control model under scenario s and all parameters in the first constraint condition; wherein all parameters in the first constraint condition include all parameters of network constraints, all parameters of distributed generation reactive capacity constraints, all parameters of on-load tap-changing transformer constraints, all parameters of shunt capacitor bank operation constraints, all parameters of electric vehicle (EV) constraints, and all parameters of electric vehicle (EV) charging station constraints.

[0050] Among them, the distribution network intraday stage T 2 The operating parameters specifically include: all parameters of the second objective function of establishing the distribution network voltage intraday control model and all parameters of the second constraint conditions; among which, all parameters of the second constraint conditions include all parameters of network constraints, all parameters of distributed power source reactive capacity constraints, and all parameters of electric vehicle (EV) constraints.

[0051] Among them, the real-time stage of the distribution network is T 3 The operating parameters specifically include: all parameters of the third objective function for establishing the real-time control model of distribution network voltage and all parameters of the third constraint conditions; wherein all parameters of the third constraint conditions include all parameters of distributed power source active power reduction constraints, all parameters of network constraints, all parameters of distributed power source reactive capacity constraints and all parameters of electric vehicle (EV) constraints.

[0052] Step 3: Establish a day-ahead voltage control model for the distribution network under scenario s according to the day-ahead operation parameters of the distribution network, and solve the day-ahead voltage control model to obtain the day-ahead set value of the reactive voltage compensation equipment.

[0053] The day-ahead control model of the distribution network voltage under scenario s includes a first objective function and a first constraint condition. The first objective function includes minimizing the switching times of reactive power regulation equipment and minimizing power loss.

[0054]

[0055] in, represents the switching times of the mth and nth shunt capacitor banks and on-load tap-changing transformers respectively; N and M represent the total number of shunt capacitor banks and on-load tap-changing transformers respectively; α and β represent the weight coefficients of on-load tap-changing transformers and shunt capacitor banks; T 1 is the number of time periods for the day-ahead scheduling; L is the number of branches in the distribution network; r l is the resistance of the distribution network line l; is the current of branch l during period t under scenario s.

[0056] The first constraints include:

[0057] 1) Network constraints:

[0058]

[0059]

[0060]

[0061]

[0062] Among them, V min , V max are the lower and upper limits of the node voltage respectively; I max are the upper limits of branch current respectively; For scene s 1 The voltage at node i at time instant, For scene s 1 The branch current flowing through node i at any moment; For scene s 1 The voltage at node j at time instant; For scene s 1 The active power and reactive power transmitted between nodes i and j at a given moment, For scene s 1 The current between nodes i and j at the moment; r ij 、x ij 、z ij t 1 The resistance, reactance, and impedance between nodes i and j at a given moment; t 1 Active network loss, load active power, electric vehicle charging power, photovoltaic output, and wind turbine output at time node i; P t Grid Refers to the active power input from the power grid; Respectively refers to t 1 At time instant, node i consumes reactive power, load consumes reactive power, photovoltaic consumes reactive power, wind turbine consumes reactive power, the shunt capacitor bank injects reactive power into node i, the on-load tap-changing transformer injects reactive power into node i, and the grid provides reactive power.

[0063] 2) Distributed power generation reactive capacity constraints:

[0064]

[0065] In the formula, Indicates that at time t 1The reactive power absorbed or provided by the xth DG; S max DG is the apparent power of the distributed power inverter, is the active power provided by the xth DG at time t1; and Respectively represent the lower and upper limits of the power factor of the distributed power supply, cosφ DG,t It represents the distributed generation at time t 1 power factor; since the distributed power generation DG includes photovoltaic PV and wind turbine WG, the above-mentioned distributed power generation reactive capacity constraint is applicable to both PV and WG.

[0066] 3) Constraints of on-load tap-changing transformers:

[0067]

[0068] Where V S is the primary substation voltage, V Tap is the voltage regulation step size, t 1 The tap position of the on-load tap-changing transformer at all times, is time t 1 The secondary output voltage of the on-load tap-changing transformer; T min , T max They are the minimum and maximum positions of the tap adjustment of the on-load tap-changing transformer respectively; is time t 1 The number of tap adjustments of the on-load tap-changing transformer; σ Tap,max The maximum number of tap adjustments for the on-load tap-changing transformer.

[0069] 4) Parallel capacitor bank operation constraints:

[0070]

[0071] In the formula, is the number of capacitors switched in each capacitor group, Q c Indicates the reactive power that a single parallel capacitor bank can provide; It means that the capacitor bank i at time t 1 Output reactive power, Q Cap,max Indicates the upper limit of reactive power output of a single capacitor bank; is time t 1 The number of switched groups of parallel capacitor banks, σ Tap,max It is the maximum switching times of the parallel capacitor bank.

[0072] 5) Electric Vehicle (EV) Constraints:

[0073]

[0074] In the formula, Indicates the previous moment x 1 Current power of EVs, Indicates the previous moment x 1 EV charging power, Indicates the xth 1 Current power of EV, η Ch is the charging efficiency, Δt 1 Indicates charging time; E min 、E max It is the lower and upper limits of EV power; Charging power for EV, The upper limit of EV charging power; is the charging status of the EV, 1 means charging, 0 means EV is not charging, SOC min , SOC max , SOC exp Indicates the lower and upper limits of the EV battery state of charge and the expected state of charge value when the EV is off-grid; t out is the time when EV is off-grid, E N Indicates the rated capacity of a single EV.

[0075] 6) Electric vehicle (EV) charging station constraints:

[0076] n s,t ≤N S

[0077] Where n s,t represents the number of EVs connected to charging station S, N S Indicates the upper limit of the number of charging stations S that can be connected.

[0078] Step 4: Based on the distribution network voltage intraday control model established according to the distribution network intraday operation parameters, the distribution network voltage intraday control model is solved to obtain the intraday setting value of the reactive voltage compensation device.

[0079] The distribution network voltage intraday control model includes the second objective function and the second constraint condition. The second objective function is to minimize the voltage deviation of the distribution network voltage node:

[0080]

[0081] Where, T 2 The number of time periods optimized for the intraday phase, V low_ref , V upper_ref are the lower and upper limits of the node voltage respectively, is the node j at time t in scene s 2 Voltage value; n jRepresents the number of node j, where j is the node sequence number.

[0082] The second constraint includes:

[0083] 1) Network constraints:

[0084]

[0085]

[0086]

[0087]

[0088] Where V min , V max are the upper and lower limits of the node voltage respectively; I max are the upper and lower limits of branch current respectively; For scene s 2 The voltage at node i at time instant, For scene s 2 The branch current flowing through node i at any moment, For scene s 2 The voltage at node j at time instant, Scene s next t 2 The active power and reactive power transmitted between nodes i and j at a given moment, For scene s 2 The current between nodes i and j at the moment; r ij 、x ij 、z ij t 2 The resistance, reactance, and impedance between nodes i and j at a given moment; t 2 Active network loss, load active power, electric vehicle charging power, photovoltaic output, and wind turbine output at time node i; Refers to the active power input from the power grid; Respectively refers to t 2 At the moment, the reactive power consumed by node i, the reactive power consumed by the load, the reactive power consumed by the photovoltaic power plant, the reactive power consumed by the wind turbine, the reactive power injected by the shunt capacitor bank into node i, and the reactive power injected by the on-load tap-changing transformer into node i; t 2 The grid provides reactive power at all times.

[0089] 2) Distributed power generation reactive capacity constraints:

[0090]

[0091] In the formula, Indicates that at time t2 The reactive power absorbed or provided by the xth DG at the moment, S max DG is the apparent power of the distributed power inverter, Indicates t 2 The active power provided by the xth DG at the moment; and They represent the lower and upper limits of the power factor of the distributed power source respectively. It represents the distributed generation at time t 2 power factor; since the distributed power generation DG includes photovoltaic PV and wind turbine WG, the above-mentioned distributed power generation reactive capacity constraint is applicable to both PV and WG.

[0092] 3) Electric vehicle restrictions:

[0093]

[0094] In the formula, Indicates the previous moment x 1 Current power of EVs, Indicates the previous moment x 1 EV charging power, Indicates the xth 1 Current power of EV, η Ch is the charging efficiency, Δt 2 Indicates charging time; E min 、E max It is the lower and upper limits of EV power; Charging power for EV; The upper limit of EV charging power; The charging status of the EV, 1 means charging, 0 means the EV is not charging, SOC min , SOC max , SOC exp Indicates the lower and upper limits of the EV battery state of charge and the expected state of charge value when the EV is off-grid; t out is the time when EV is off-grid, E N Indicates the rated capacity of a single EV.

[0095] Step 5: Establish a real-time control model of the distribution network voltage according to the real-time operation parameters of the distribution network, and solve the real-time control model of the distribution network voltage to obtain a real-time active power control reference value of the distributed power source;

[0096] The distribution network voltage real-time control model includes a third objective function and a third constraint condition. The third objective function includes a function for minimizing the voltage deviation of the distribution network voltage nodes and minimizing the active power reduction of the distributed power sources:

[0097]

[0098]

[0099] Where, T 3 represents the number of time periods in the real-time phase, is the node j at time t in scene s 3 The voltage value, V low_ref , V upper_ref are the lower and upper limits of the node voltage respectively; Represents photovoltaic access point j 1 The active power reduction amount, Indicates the wind turbine access point j 2 The active power reduction amount.

[0100] The third constraint includes:

[0101] 1) Distributed power generation active power reduction constraints:

[0102]

[0103] In the formula, Represent the active power reduction of wind turbines and photovoltaics respectively; P w.max , P p.max They represent the upper limits of active power reduction for wind turbines and photovoltaics respectively.

[0104] 2) Network constraints:

[0105]

[0106]

[0107]

[0108]

[0109] Where V min , V max are the upper and lower limits of the node voltage respectively; I max are the upper and lower limits of branch current respectively; For scene s 3 The voltage at node i at time For scene s 3 The branch current flowing through node i at any moment, For scene s, t 3 The voltage at node j; Scene s next t 3 The active power and reactive power transmitted between nodes i and j at a given moment, For scene s 3 The current between nodes i and j at the moment; rij 、x ij 、z ij t 2 The resistance, reactance, and impedance between nodes i and j at a given moment; t 3 Active network loss, load active power, electric vehicle charging power, photovoltaic output, and wind turbine output at time node i; Refers to the active power input from the power grid; Respectively refers to t 3 At the moment, the reactive power consumed by node i, the reactive power consumed by the load, the reactive power consumed by the photovoltaic power plant, the reactive power consumed by the wind turbine, the reactive power injected by the shunt capacitor bank into node i, and the reactive power injected by the on-load tap-changing transformer into node i; t 3 The grid provides reactive power at all times.

[0110] 3) Distributed power generation reactive capacity constraints:

[0111]

[0112] In the formula, Indicates that at time t 3 Reactive power absorbed or provided by DG; S max DG is the apparent power of the distributed power inverter, is time t 3 The active power provided by the xth DG; and They represent the lower and upper limits of the power factor of the distributed power source respectively. It represents the distributed generation at time t 3 power factor; since the distributed power generation DG includes photovoltaic PV and wind turbine WG, the above-mentioned distributed power generation reactive capacity constraint is applicable to both PV and WG.

[0113] 4) Electric Vehicle (EV) Constraints:

[0114]

[0115] In the formula, Indicates the previous moment x 1 Current power of EVs, Indicates the previous moment x 1 EV charging power, Indicates the xth 1 Current power of EV; η Ch is the charging efficiency; Δt 3 Indicates charging time; E min 、E max It is the lower and upper limits of EV power; is the EV charging power (positive value); The upper limit of EV charging power; is the charging status of the EV, 1 means charging, 0 means EV is not charging, SOC min , SOC max , SOC exp Indicates the lower and upper limits of the EV battery state of charge and the expected state of charge value when the EV is off-grid; t out is the time when EV is off-grid, E N Indicates the rated capacity of a single EV.

[0116] Step 6: Adjust the reactive power compensation equipment in the day-ahead stage according to the day-ahead set value of the reactive voltage compensation equipment, and adjust the reactive power output of the distributed power source in the day-ahead stage according to the intra-day reactive power control reference value of the distributed power source; when the voltage of the distribution network node still exceeds the voltage limit, adjust the active power output of the distributed power source according to the real-time active power control reference value of the distributed power source.

[0117] Among them, the day-ahead set value of the reactive voltage compensation equipment includes the number of switched groups of parallel capacitor groups and the tap position of the on-load tap-changing transformer; the intraday reactive power control reference value of the distributed power source includes the reactive power output of photovoltaics, the reactive power output of wind turbines and the active power of electric vehicles; the real-time active power control reference value of the distributed power source includes the active power output of photovoltaics and the active power output of wind turbines.

[0118] like Figure 2 As shown in the figure, it is a schematic diagram of the IEEE33 node distribution network provided by an embodiment of the present invention, and the voltage adjustment range of the on-load tap-changing transformer is ± 5%, a total of 21 taps; nodes 11, 20, and 29 are equipped with 5 groups of parallel capacitors, each with a capacity of 60kVar; nodes 18, 25, and 33 are connected to wind turbines; nodes 8, 14, and 22 are connected to photovoltaics; nodes 9, 23, and 32 are connected to electric vehicle charging stations. The total number of electric vehicles is 200, and the charging numbers are 70%, 10%, and 20% of the total number respectively.

[0119] The present invention takes the voltage exceeding the limit of some nodes after the distribution network is connected to photovoltaic, wind turbines, and electric vehicles as the scenario, and verifies the voltage exceeding the limit of the nodes before and after the control. Figure 3 (a) and Figure 3 (b) are the initial voltage distribution of feeder 1 in the IEEE33-node distribution network and the voltage distribution after control, respectively. The horizontal axis in the figure is time, and the vertical axis is the per-unit value of the effective value of the feeder node voltage. It can be seen that the voltage of nodes 14 and 18 of feeder 1 exceeded the upper limit at around 12 noon, and the voltage exceeded the lower limit at around 18:00. On the one hand, these nodes are connected The installed capacity of DG installed is large. On the other hand, because the node is at the end of the feeder, the voltage is more likely to exceed the limit than other nodes. After executing the voltage control strategy of the method of the present invention, the voltage of node 14 drops from 1.074pu to 1.04pu at 12:00; the voltages of nodes 14 and 18 drop from 0.491pu and 0.948pu to 0.961pu and 0.965pu respectively at 18:00, both of which are within the expected voltage range; the voltage fluctuations of other nodes that do not exceed the limit are reduced. Overall, the voltage exceeding the limit situation is improved.

[0120] The present invention generates a typical distribution network scenario that takes source and load uncertainty into consideration, establishes and solves a corresponding distribution network voltage day-ahead control model, a distribution network voltage intraday control model and a distribution network voltage real-time control model for the typical distribution network scenario, and adjusts reactive compensation equipment in the day-ahead stage, adjusts reactive power output of distributed power sources in the intraday stage, and adjusts active power output of distributed power sources in real time according to set values ​​obtained by the solution; the present invention provides a distribution network multi-time scale voltage control method that takes source and load uncertainty into consideration, fully considers the response time differences of different voltage compensation devices, and can effectively suppress node voltage over-limit caused by multiple uncertain source and load connections to the distribution network.

[0121] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A multi-time scale voltage control method for distribution network considering source and load uncertainty, characterized in that The following steps are involved: Determine the distribution network scenario s through distribution network scenario generation and distribution network scenario reduction; Collect the day-ahead phase operation parameters, the intraday phase operation parameters and the real-time phase operation parameters of the distribution network; According to the day-ahead operation parameters of the distribution network, a day-ahead voltage control model of the distribution network under scenario s is established, and the day-ahead voltage control model of the distribution network is solved to obtain the day-ahead set value of the reactive voltage compensation equipment; According to the daily operation parameters of the distribution network, a distribution network voltage daily control model is established, and the distribution network voltage daily control model is solved to obtain the daily reactive power control reference value of the distributed generation; A real-time control model of distribution network voltage is established according to the real-time operation parameters of the distribution network, and the real-time control model of distribution network voltage is solved to obtain the real-time active power control reference value of the distributed power source; Adjust the reactive power compensation equipment in the day-ahead stage according to the day-ahead set value of the reactive voltage compensation equipment, and adjust the reactive power output of the distributed power source in the day-ahead stage according to the intra-day reactive power control reference value of the distributed power source; When the voltage at the distribution network node still exceeds the voltage limit, the active power output of the distributed power source is adjusted according to the real-time active power control reference value of the distributed power source.

2. A distribution network multi-time scale voltage control method considering source and load uncertainty according to claim 1, characterized in that: The distribution network scenario generation includes: A source-load basic scenario set is established based on the wind speed state, light intensity state and electric vehicle state represented by the time series, and the source-load basic scenario set is sorted by Cholesky to obtain the distribution network scenario generation.

3. A distribution network multi-time scale voltage control method considering source and load uncertainty according to claim 1, characterized in that: The distribution network scenario reduction includes: Randomly select K scenarios in the distribution network scenario generation as cluster centers; Traverse the remaining scenarios except K scenarios in the distribution network scenario generation, calculate the Euclidean distance from the remaining scenarios to the cluster center, determine the central scenario of the cluster center closest to the remaining scenarios, and divide the remaining scenarios into the classes represented by the central scenario with the closest Euclidean distance to generate new K cluster centers; The average value group of the new K cluster centers is used as the new central scene for the next iteration until the central scene remains unchanged or the number of iterations reaches the upper limit, and the scene closest to the central scene among the K cluster centers is selected as the typical scene.

4. A distribution network multi-time scale voltage control method considering source and load uncertainty according to claim 3, characterized in that: The scenario with the highest probability of occurrence in the typical scenarios is the distribution network scenario s.

5. The method for multi-time-scale voltage control of a distribution network considering source-load uncertainty according to claim 1, characterized in that: The day-ahead control model of the distribution network voltage under the scenario s includes a first objective function and a first constraint condition; The first objective function includes minimizing power loss and minimizing the number of switching times of reactive power regulation equipment; the first constraint conditions include: network constraints, distributed power generation reactive capacity constraints, on-load tap-changing transformer constraints, parallel capacitor bank operation constraints, electric vehicle constraints and electric vehicle charging station constraints.

6. A distribution network multi-time scale voltage control method considering source and load uncertainty according to claim 1, characterized in that: The distribution network voltage intraday control model includes a second objective function and a second constraint condition; The second objective function is to minimize the voltage deviation of the distribution network voltage node; the second constraint conditions include: network constraints, distributed power source reactive capacity constraints and electric vehicle constraints.

7. A distribution network multi-time scale voltage control method considering source and load uncertainty according to claim 1, characterized in that: The distribution network voltage real-time control model includes a third objective function and a third constraint condition; The third objective function includes functions for minimizing the voltage deviation of distribution network voltage nodes and minimizing the distributed active power reduction; the third constraint conditions include: distributed energy active power reduction constraint, network constraint, distributed power source reactive capacity constraint and electric vehicle constraint.

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