Power fluctuation suppression method and device for network point under power distribution network

Abstract

The application is suitable for the technical field of power system control, and provides a power fluctuation suppression method and device for a distribution network. The method comprises the following steps: obtaining source and load prediction values for the day before, solving a preset power optimization model according to the source and load prediction values, and obtaining optimal power for the day before a network point; determining a power tracking value for the network point according to the optimal power for the network point and power measurement values for the network point within the day; obtaining source and load measurement values within the day, solving a preset output optimization model according to the power tracking value for the network point and the source and load measurement values, and obtaining optimal output values of fast regulation and control equipment in the distribution network; and controlling the fast regulation and control equipment according to the optimal output values of the fast regulation and control equipment to control active power of a feeder load and suppress power fluctuation of the network point in the distribution network. The application can control the output values of the fast regulation and control equipment, make the feeder load participate in power fluctuation suppression of the network point in the distribution network, and effectively realize power fluctuation suppression of the network point in the distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system control, in particular to a power fluctuation suppression method and device for a distribution network. BACKGROUND

[0002] In recent years, with the proposal of the double carbon target, the construction of new power systems and energy transformation continue to advance, which has effectively promoted the rapid and vigorous development of the new energy industry. Among them, the distributed new energy installed capacity in the distribution network is rapidly growing, and its randomness and volatility will lead to active imbalance of the system, and through the downlink point connected with the main network, it will be transmitted to the main network, which brings difficulties to the stable operation of the power system. Therefore, it is very important to suppress the power fluctuation of the downlink point of the distribution network.

[0003] At present, the power fluctuation of the downlink point of the distribution network is usually suppressed by adjusting the source side resources. However, with the increase in the number of new energy equipment, the method of adjusting the source side resources cannot effectively suppress the power fluctuation of the downlink point of the distribution network, thereby leading to unstable operation of the power system. SUMMARY

[0004] Therefore, the embodiments of the present application provide a power fluctuation suppression method and device for a downlink point of a distribution network to solve the technical problem that the related art cannot effectively suppress the power fluctuation of the downlink point of the distribution network.

[0005] In a first aspect, the embodiments of the present application provide a power fluctuation suppression method for a downlink point of a distribution network, comprising:

[0006] Obtaining a source-load prediction value in the day before, solving a preset power optimization model according to the source-load prediction value, and obtaining an optimal power of the downlink point in the day before; the power optimization model takes the sum of the difference between the estimated power of the downlink point corresponding to two adjacent periods and the adjustment amount of the slow regulation and control device in the distribution network as the first objective function;

[0007] Determining a power tracking value of the downlink point according to the optimal power of the downlink point and the power measurement value of the downlink point in the day;

[0008] Obtaining a source-load measurement value in the day, solving a preset output optimization model according to the power tracking value of the downlink point and the source-load measurement value, and obtaining an optimal output value of the fast regulation and control device in the distribution network; the output optimization model takes the difference between the power tracking value of the downlink point and the actual value of the power of the downlink point in the day as the second objective function;

[0009] Controlling the fast regulation and control device according to the optimal output value of the fast regulation and control device to control the active power of the feeder load, and suppressing the power fluctuation of the downlink point of the distribution network.

[0010] In a possible implementation, the power optimization model takes the adjustment amount of a slow regulating device as a decision variable, takes the alternating current flow constraint, the first node voltage constraint, the first active power balance constraint, the first lower network node active power constraint, the capacitor operation constraint, the on-load tap-changing transformer operation constraint, and the first feeder load voltage-power coupling characteristic constraint as the first constraint condition; the slow regulating device includes a capacitor and an on-load tap-changing transformer in the power distribution network.

[0011] The solving of the preset power optimization model according to the source and load prediction value includes:

[0012] The first objective function of the power optimization model is solved based on the first constraint condition according to the source and load prediction value, to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer.

[0013] The lower network node optimal power is obtained according to the lower network node estimated power corresponding to the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer.

[0014] In a possible implementation, the expression of the first objective function is:

[0015]

[0016] In the formula, is the lower network node estimated power in the k period, is the lower network node estimated power in the k-1 period, is the switching state of the capacitor of the i node in the k period, the capacitor is switched on as 1, and the capacitor is switched off as 0; V0 is the rated voltage, is the output voltage of the on-load tap-changing transformer of the i node in the k period; each period is 15 minutes, k ∈ T, T is a time set, and one day is taken; α, β, and γ are weight coefficients, N CB is a capacitor-containing node set, N OLTC is an on-load tap-changing transformer-containing node set.

[0017] In a possible implementation, the determination of the lower network node power tracking value according to the lower network node optimal power and the lower network node power measurement value in the day includes:

[0018] The lower network node optimal power and the lower network node power measurement value are subjected to linear interpolation processing;

[0019] The lower network node power tracking value is obtained according to the interpolation processing result.

[0020] In a possible implementation, the expression of the lower network node power tracking value is:

[0021]

[0022] Popt (t) = Popt (t) + Popt (t) - Popt (t) (1) t pcc,tar Popt (t) is the power tracking value of the lower node in the t period, Pmeas (k) is the power measurement value of the lower node in the k period, Popt (k) is the optimal power of the lower node in the k period; each period is 1 minute, each period is 15 minutes, t∈k, k∈T, T is a time set, and one day is taken.

[0023] In a possible implementation, the output optimization model takes the output quantity of the fast regulating device as a decision variable, and takes the second node voltage constraint, the second active power balance constraint, the second lower node active power constraint, the photovoltaic inverter operation constraint, the static var compensator operation constraint, the voltage change constraint, and the second feeder load voltage-power coupling characteristic constraint as the second constraint condition; wherein the fast regulating device includes a photovoltaic inverter and a static var compensator in the power distribution network;

[0024] The output optimization model is solved according to the lower node power tracking value and the source and load measurement value, to obtain the optimal output quantity of the fast regulating device in the power distribution network, including:

[0025] According to the lower node power tracking value and the source and load measurement value, the second objective function of the output optimization model is solved based on the second constraint condition, to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator.

[0026] In a possible implementation, the expression of the second objective function is:

[0027]

[0028] Popt (t) = Popt (t) + Popt (t) - Popt (t) (1) t pcc,tar Popt (t) is the power tracking value of the lower node in the t period, P t pcc Popt (t) is the actual power value of the lower node in the t period, P t pcc determined according to the reactive power of the photovoltaic inverter and the reactive power of the static var compensator; t∈h, h∈T, h is an optimization period, each period is 1 minute, each optimization period is 15 minutes, T is a time set, and one day is taken.

[0029] In a possible implementation, the fast regulating device is controlled according to the optimal output quantity of the fast regulating device, including:

[0030] The output of the photovoltaic inverter in the power distribution network is controlled according to the optimal reactive power of the photovoltaic inverter;

[0031] The output of the static var compensator in the distribution network is controlled according to the optimal reactive power of the static var compensator.

[0032] Secondly, embodiments of this application provide a power fluctuation mitigation device for distribution network points, comprising:

[0033] The acquisition module is used to acquire the source load prediction value of the day before, and solve the preset power optimization model based on the source load prediction value to obtain the optimal power of the downstream point of the day before; the power optimization model takes the sum of the difference between the predicted power of the downstream point corresponding to two adjacent cycles and the adjustment amount of the slow control equipment in the distribution network as the first objective function.

[0034] The determination module is used to determine the power tracking value of the downstream network point based on the optimal power of the downstream network point and the power measurement value of the downstream network point within the day;

[0035] The module is used to obtain the measured values ​​of source load within the day. Based on the power tracking value of the downstream network point and the measured value of source load, the preset output optimization model is solved to obtain the optimal output of the fast control equipment in the distribution network. The output optimization model takes minimizing the difference between the power tracking value of the downstream network point and the actual power value of the downstream network point within the day as the second objective function.

[0036] The control module is used to control the fast control device according to the optimal output of the fast control device, so as to control the active power of the feeder load and smooth the power fluctuation of the distribution network.

[0037] In one possible implementation, the power optimization model uses the adjustment amount of the slow-regulating equipment as the decision variable, and the first constraint conditions are AC power flow constraints, first node voltage constraints, first active power balance constraints, first downstream active power constraints, capacitor operation constraints, on-load tap-changing transformer operation constraints, and first feeder load voltage-power coupling characteristic constraints; wherein, the slow-regulating equipment includes capacitors and on-load tap-changing transformers in the distribution network;

[0038] The acquisition module is further configured to solve the first objective function of the power optimization model based on the first constraint condition according to the source load prediction value, so as to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer.

[0039] The optimal power at the lower grid point is obtained based on the optimal switching state of the capacitor and the estimated power at the lower grid point corresponding to the optimal output voltage of the on-load tap-changing transformer.

[0040] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0041] The distribution network power fluctuation mitigation method and apparatus provided in this application pre-schedules slow control equipment based on source load forecasts during the day-ahead phase to obtain the optimal power at the distribution network point. During the intraday phase, based on the optimal power at the distribution network point and the intraday power measurement value at the distribution network point, a power tracking value at the distribution network point is determined. Then, with the goal of making the actual intraday power value at the distribution network point as close as possible to the power tracking value at the distribution network point, the optimal output of the fast control equipment is determined based on the power tracking value at the distribution network point. Thus, the fast control equipment is controlled according to the above-mentioned optimal output value to control the active power of the feeder load, so that the feeder load participates in the power fluctuation mitigation of the distribution network point, thereby effectively realizing the power fluctuation mitigation of the distribution network point.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0045] Figure 2 This is a flowchart illustrating a method for suppressing power fluctuations at distribution network points provided in an embodiment of this application.

[0046] Figure 3 This is a schematic diagram of the structure of a power fluctuation mitigation device for a distribution network under an embodiment of this application. Detailed Implementation

[0047] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0048] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0049] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0050] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0051] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0052] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0053] Based on the idea of ​​effectively mitigating power fluctuations at distribution network points, the inventors discovered that the regulation potential of load-side resources can be tapped to enrich the control methods of the distribution network. For example, the feeder loads on the distribution network side are important load-side resources, and their voltage and active power have strong coupling characteristics. Therefore, the feeder loads can participate in mitigating power fluctuations at distribution network points.

[0054] Specifically, during the day-ahead phase, slow-regulation equipment can be pre-scheduled based on the source-load forecast value to obtain the optimal power of the downstream network point. During the intraday phase, the downstream network point power tracking value is determined based on the optimal power of the downstream network point and the intraday downstream network point power measurement value. Then, with the goal of making the intraday downstream network point power value as close as possible to the downstream network point power tracking value, the optimal output of the fast-regulation equipment is determined based on the downstream network point power tracking value. The fast-regulation equipment is controlled according to the above optimal output value to control the active power of the feeder load, so that the feeder load participates in the smoothing of the downstream network point power fluctuations, effectively realizing the smoothing of the downstream network point power fluctuations.

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0056] First refer to Figure 1 , Figure 1The application scenario of this application is illustrated schematically. In this application scenario, an electronic device is provided with a preset power optimization model and an output optimization model.

[0057] Electronic equipment acquires the predicted source and load values ​​for the current day, solves the preset power optimization model based on the predicted source and load values ​​to obtain the optimal power of the downstream points for the current day, and determines the power tracking value of the downstream points based on the optimal power of the downstream points and the measured power values ​​of the downstream points during the day. Then, it acquires the measured source and load values ​​for the day, solves the preset output optimization model based on the power tracking value of the downstream points and the measured source and load values ​​to obtain the optimal output of the fast control equipment in the distribution network, and controls the fast control equipment based on the optimal output of the fast control equipment to control the active power of the feeder load and smooth the power fluctuations of the downstream points of the distribution network.

[0058] Among them, the power optimization model aims to minimize the difference between the estimated power of the downstream points corresponding to two adjacent cycles and the sum of the adjustment amounts of the slow control equipment in the distribution network, while the output optimization model aims to minimize the difference between the power tracking value of the downstream points and the actual power value of the downstream points within the day.

[0059] The following is combined with Figure 1 ,refer to Figure 2 This application describes a method for mitigating power fluctuations at distribution network points according to exemplary embodiments thereof.

[0060] Figure 2 This is a flowchart illustrating a method for mitigating power fluctuations at distribution network points according to an embodiment of this application. Figure 2 As shown, the method in the embodiments of this application may include:

[0061] Step 201: Obtain the source load prediction value for the day before, and solve the preset power optimization model based on the source load prediction value to obtain the optimal power of the next grid point for the day before.

[0062] The power optimization model takes minimizing the difference between the estimated power of the downstream points corresponding to two adjacent cycles and the sum of the adjustment amounts of the slow-regulating equipment in the distribution network as the first objective function, the adjustment amount of the slow-regulating equipment as the decision variable, and the AC power flow constraints, the first node voltage constraints, the first active power balance constraints, the first downstream active power constraints, the capacitor operation constraints, the on-load tap-changing transformer operation constraints, and the first feeder load voltage-power coupling characteristic constraints as the first constraint conditions.

[0063] Here, slow-regulation equipment includes capacitor banks (CBs) and on-load tap-changing transformers (OLTCs) in the distribution network. Correspondingly, the regulation quantities of slow-regulation equipment include the switching status of the capacitors and the output voltage of the OLTCs. When the switching status of the capacitors and the output voltage of the OLTCs change, the power at the downstream points also changes. Therefore, by pre-regulating the switching status of the capacitors and the output voltage of the OLTCs, the estimated power at the corresponding downstream points can be obtained.

[0064] In some embodiments, when the optimal power of the downstream grid point is obtained before the current day, the first objective function of the power optimization model can be solved based on the source load prediction value and the first constraint condition to obtain the sum of the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer. Based on the estimated power of the downstream grid point corresponding to the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer, the optimal power of the downstream grid point is obtained.

[0065] The expression for the first objective function is:

[0066]

[0067] In the formula, This represents the estimated power of the network nodes over period k, which is also the estimated power of the network nodes in the current period. The estimated power of the network node in period k-1 is the estimated power of the network node in the previous period. This represents the switching state of the capacitor at node i during cycle k; the capacitor is 1 when switched on and 0 when switched off. V0 is the rated voltage. Let N be the output voltage of the on-load tap changer at node i during cycle k (corresponding to the tap position of the on-load tap changer); each cycle is 15 minutes, which means the optimization interval is 15 minutes, k∈T, where T is the time set, taken as one day; α, β, and γ are weighting coefficients, N CB Let N be the set of nodes containing capacitors. OLTC This is a set of nodes containing on-load tap-changing transformers.

[0068] In this embodiment, based on the source-load prediction value during the day-ahead phase, the power optimization model is solved at an optimization interval (cycle) of 15 minutes to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer. The estimated power of the next grid point corresponding to the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer is the optimal power of the next grid point.

[0069] In other words, during the day-ahead phase, the pre-scheduled capacitors and on-load tap-changing transformers are used to determine the switching status of the capacitors and the output voltage (tap position) of the on-load tap-changing transformers in each cycle. This is done to minimize the sum of the difference in estimated power at the downstream distribution point between two adjacent cycles and the adjustment amount of the slow-control equipment in the distribution network, thereby obtaining the optimal power at the downstream distribution point every 15 minutes, which is to say, obtaining the optimal power sequence at the downstream distribution point. After obtaining the optimal power at the downstream distribution point, this optimal power is used as the data support for intraday optimization.

[0070] It should be noted that source load data is required to solve the power optimization model. However, since this is the current daytime period, the source load data used to solve the power optimization model are predicted values, i.e., predicted source load values. In other words, the predicted source load values ​​are the source load data required to solve the power optimization model.

[0071] As can be seen from the foregoing, the first constraint conditions may include AC power flow constraints, first node voltage constraints, first active power balance constraints, first downstream active power constraints, capacitor operation constraints, on-load tap-changing transformer operation constraints, and first feeder load voltage-power coupling characteristic constraints.

[0072] The expression for the current flow constraint is:

[0073]

[0074] In the formula, The estimated power at the network point under period k is the active power. For k-period, estimate the reactive power at the network point; and These represent the active power and reactive power of the photovoltaic inverter at node i in cycle k, respectively. and Let be the active power and reactive power of the load at node i in period k, respectively. e represents the reactive power of the capacitor at node i during period k; i,k and f i,k Let e ​​be the real and imaginary parts of the node voltage at node i in period k, respectively. j,k and f j,k Let G be the real and imaginary parts of the node voltage at node j in period k, respectively. i,j and B i,j Let P and Q represent the real and imaginary parts of the line admittance between node i and node j, respectively; N is the set of nodes, and T is the set of time. It should be noted that in this embodiment, P represents active power and Q represents reactive power.

[0075] The expression for the voltage constraint at the first node is:

[0076]

[0077] In the formula, V i,k,0 Let i be the initial measured voltage at node i during period k. V represents the voltage increment at node i during period k under the combined influence of the capacitor and the on-load tap-changing transformer. min and V max These represent the minimum and maximum values ​​of the node voltage, respectively.

[0078] The expression for the first active power balance constraint is:

[0079]

[0080] In the formula, For the k-period system network loss, N PV This refers to a set of nodes containing photovoltaic inverters. During the day-ahead phase, the distribution network only considers photovoltaic output and load forecasting.

[0081] The expression for the active power constraint at the first substation is:

[0082]

[0083] In the formula, and These are the lower and upper limits of the active power at the downstream network point, respectively.

[0084] Optionally, to extend the lifespan of the capacitor, a constraint should be imposed on the maximum number of daily switching changes. The expression for the capacitor operation constraint is:

[0085]

[0086] In the formula, Let i be the switching state of the capacitor at node i during period k-1. The rated reactive power of the capacitor at node i is... This is the threshold for the maximum number of switching changes per day for the capacitor.

[0087] The expression for the operating constraints of on-load tap-changing transformers is:

[0088]

[0089]

[0090] In the formula, and Let be the output voltage and input voltage of the on-load tap-changing transformer at node i during cycle k, respectively. Let i be the tap position of the on-load tap-changing transformer at node i during cycle k. For the tap position of the on-load tap-changing transformer at node i in cycle k-1, V tapThis represents the voltage change corresponding to each tap of an on-load tap-changing transformer. and These are the lower and upper limits of the tap position of the on-load tap-changing transformer at node i, respectively. This is the threshold for the maximum number of tap changes per day for an on-load tap-changing transformer.

[0091] The expression for the constraint of the voltage-power coupling characteristics of the first feeder load is:

[0092]

[0093] In the formula, and These are the voltage reduction energy-saving coefficients for active and reactive power at node i in cycle k, respectively. These coefficients can quantitatively reflect the relationship between feeder load power and voltage. and Let V be the initial voltage, initial active power, and initial reactive power of the load at node i in period k. i,k The voltage of the adjusted i-node load during period k.

[0094] It should be noted that since nodes correspond to capacitors, on-load tap-changing transformers, loads, and photovoltaic inverters, i can be used to represent node i, on-load tap-changing transformer i, load i, and photovoltaic inverter i.

[0095] Optionally, when solving the first objective function of the power optimization model based on the source load prediction value and the first constraint condition, the optimization solution can be performed using the GUROBI solver on the MATLAB platform to obtain the optimal power of the next grid point.

[0096] Step 202: Determine the power tracking value of the downstream network point based on the optimal power of the downstream network point and the power measurement value of the downstream network point during the day.

[0097] In this embodiment, the power measurement value of the offline network point is the power of the offline network point measured every 15 minutes during the day.

[0098] In some embodiments, when determining the power tracking value of the lower network point, linear interpolation can be performed on the optimal power of the lower network point and the power measurement value of the lower network point, and the power tracking value of the lower network point can be obtained based on the interpolation result.

[0099] For example, the time interval for the optimal power at the downstream distribution point is 15 minutes, and the time interval for the power measurement values ​​at the downstream distribution point is also 15 minutes. However, to effectively mitigate power fluctuations at the downstream distribution points, this embodiment sets the intraday optimization time interval to 1 minute. Therefore, by utilizing the principle of linear interpolation and combining the optimal power at the downstream distribution point with the measured power values, the power tracking value at the downstream distribution point is obtained.

[0100] The expression for the power tracking value at the next network point is:

[0101]

[0102] In the formula, P t pcc,tar The power tracking value of the network point during time period t. The measured power value at the network point under period k. The optimal power of the network point under period k; each time period is 1 minute, each period is 15 minutes, t∈k, k∈T, T is the time set, which is one day.

[0103] In this way, the optimal output of the fast control equipment can be determined based on the power tracking value of the downstream network point, so that the actual power value of the downstream network point within the day is as close as possible to the power tracking value of the downstream network point.

[0104] Step 203: Obtain the measured values ​​of source load during the day. Based on the power tracking values ​​of the downstream points and the measured values ​​of source load, solve the preset output optimization model to obtain the optimal output of the fast control equipment in the distribution network.

[0105] The output optimization model takes minimizing the difference between the power tracking value of the downstream grid point and the actual power value of the downstream grid point within the day as the second objective function, takes the output of the fast control equipment as the decision variable, and takes the second node voltage constraint, the second active power balance constraint, the second downstream grid point active power constraint, the photovoltaic inverter operation constraint, the static var compensator operation constraint, the voltage change constraint, and the second feeder load voltage-power coupling characteristic constraint as the second constraint conditions.

[0106] Here, fast-regulation equipment includes photovoltaic (PV) inverters and static var compensators (SVCs) in the distribution network. Correspondingly, the output of fast-regulation equipment includes the reactive power of the PV inverters and the reactive power of the SVCs. When the reactive power of the PV inverters and the SVCs changes, the active power of the corresponding feeder loads also changes. Therefore, by regulating the reactive power of the PV inverters and the SVCs, the active power of the feeder loads can be regulated, so that the actual power value at the downstream distribution point within the day is as close as possible to the power tracking value at the downstream distribution point, thereby achieving the smoothing of power fluctuations at the downstream distribution points.

[0107] In some embodiments, when the optimal output of the fast control device in the distribution network is obtained, the second objective function of the output optimization model can be solved based on the power tracking value of the downstream point and the measured value of the source load, according to the second constraint condition, to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator.

[0108] The expression for the second objective function is:

[0109]

[0110] In the formula, P t pcc,tar P represents the power tracking value at the network point during time period t. t pcc P represents the actual power value of the network point during time period t. t pcc The reactive power of the photovoltaic inverter and the static var compensator are determined; t∈h, h∈T, h is the optimization period, each time period is 1 minute, each optimization period is 15 minutes, and T is the time set, which is one day.

[0111] In this embodiment, during the intraday phase, based on the measured source load values, the output optimization model is solved at 1-minute intervals to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator. The actual power values ​​at the downstream grid point corresponding to the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator are the actual optimal power values ​​at the downstream grid point.

[0112] In other words, the reactive power of the photovoltaic inverter and the reactive power of the static var compensator are determined at each time period during the day to minimize the difference between the power tracking value and the actual power value at the downstream grid point (minimize the value of the second objective function), thereby obtaining the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator for each minute, which serves as the basis for controlling the fast regulation equipment.

[0113] It should be noted that source load data is required to solve the output optimization model. Since this is an intraday phase, the source load data used to solve the output optimization model are measured values, i.e., the measured source load values. In other words, the measured source load values ​​are the source load data required to solve the output optimization model.

[0114] As mentioned above, the second constraint conditions may include the second node voltage constraint, the second active power balance constraint, the second downstream active power constraint, the photovoltaic inverter operation constraint, the static var compensator operation constraint, the voltage change constraint, and the second feeder load voltage-power coupling characteristic constraint.

[0115] The expression for the voltage constraint at the second node is:

[0116]

[0117] In the formula, V i,t,0 Let be the initial measured voltage of node i during time period t. The voltage increment at node i during time period t is the result of the combined effects of capacitor and on-load tap-changing transformer regulation.

[0118] The expression for the second active power balance constraint is:

[0119]

[0120] In the formula, Let P be the active power of the load at node i during time period t. t loss For the system network loss during time period t, Let be the active power of the photovoltaic inverter at node i during time period t.

[0121] Optionally, the optimized actual power value of the downstream network should meet the requirement that the power fluctuation amplitude per minute is not too large. In actual control processes, active power fluctuations may occur; therefore, a certain degree of power fluctuation is permissible, using the maximum permissible deviation rate. Therefore, the expression for the active power constraint at the second lower network point is:

[0122]

[0123] In the formula, P t pcc This represents the actual power output of the network points during time period t, which is the actual power output of the network points during this time period. This represents the actual power value of the network point during time period t-1, which is the actual power value of the network point during the previous time period. and These are the lower and upper limits of the active power at the downstream network point, respectively.

[0124] The expression for the operating constraints of the photovoltaic inverter is:

[0125]

[0126] In the formula, and These are the minimum and maximum reactive power of the photovoltaic inverter at node i, respectively. and These represent the active power and reactive power of the photovoltaic inverter at node i during time period t, respectively. The reactive power regulation to be optimized for the photovoltaic inverter at node i during time period t; This represents the rated capacity of the photovoltaic inverter at node i.

[0127] The expression for the operating constraints of the static var compensator is:

[0128]

[0129] In the formula, and These are the minimum and maximum adjustable reactive power values ​​of the static var compensator at node i, respectively. Let be the reactive power of the static var compensator at node i during time period t. Let t be the reactive power compensation adjustment of the static var compensator at node i during time period t.

[0130] For example, in this embodiment, a voltage sensitivity expression is first established based on the Zbus linearized power flow model. This expression quantifies the linear analytical relationship between node voltage and the reactive power (injected power) of fast-regulating equipment. Specifically, a linearized power flow model of the Zbus nonlinear model with respect to a reference power flow point is constructed through single-step iteration. The latest operating point U in the distribution network is selected. 0 As a reference power flow point, the voltage phasor of the nodes other than the balancing node is used. When the operating state changes, that is, when the active and reactive power injection of each node changes, the node injection power is updated to S. The node voltage U is obtained by single-step iteration, and the specific form is shown in the following formula.

[0131]

[0132] In the formula, U′ is the voltage matrix of each PQ node, and S′ is the updated node injection power; Determined by both line parameters and reference power flow point; W = -Y LL -1 Y L0 U0 is determined by the line parameters and the root node voltage. LL Y is the admittance matrix of all nodes except the balancing node. L0 Let U0 be the mutual admittance matrix between each node and the slack node, and U0 be the voltage of the slack node.

[0133] Thus, by calculating the partial derivatives of the voltage at each node with respect to the injected power based on the above linearized power flow model, an expression for voltage sensitivity can be obtained. This allows for the quantification of the linear analytical relationship between node voltage and injected power, significantly reducing computational complexity.

[0134] The expression for voltage sensitivity:

[0135]

[0136] In the formula, U′ is the voltage matrix of each PQ node, P and Q are the active power matrix and reactive power matrix of each PQ node, respectively. 0 'As the latest trend point, Y' LL Let be the admittance matrix of all nodes except the slack node. Based on the expression for voltage sensitivity, the impact of reactive power output changes of regulating equipment such as photovoltaic inverters and on-load tap-changing transformers on the voltage of each node can be calculated, thereby clarifying the adjustment amount of active power of the feeder load.

[0137] Optionally, the node voltage can be quickly calculated based on the analytical voltage sensitivity calculation results. The expression for the voltage change constraint is:

[0138]

[0139] In the formula, The sensitivity of node voltage to changes in node reactive power injection, ΔQ t Let t represent the reactive power compensation change during time period t, and ΔV be a vector composed of the voltage changes at each node.

[0140] The expression for the constraint of the load voltage-power coupling characteristics of the second feeder is:

[0141]

[0142] In the formula, and These are the voltage reduction energy-saving coefficients for active and reactive power at node i during time period t, respectively. These coefficients can quantitatively reflect the relationship between feeder load power and voltage. and Let V be the initial voltage, initial active power, and initial reactive power of the load at node i during time period t. i,t The voltage of the adjusted node i load during time period t.

[0143] Optionally, when solving the second objective function of the output optimization model based on the measured source load values ​​and the second constraint conditions, since the output optimization model only has an absolute value term in the second objective function, it is relatively easy to linearize. After the above power flow linearization, voltage sensitivity analytical calculation and other steps, the output optimization model is linear as a whole. Treating it as a mixed integer linear programming problem (MILP), it can be directly optimized and solved using the GUROBI solver to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator.

[0144] Step 204: Control the fast control equipment according to the optimal output of the fast control equipment to control the active power of the feeder load and smooth the power fluctuation of the distribution network.

[0145] In some embodiments, when controlling the fast-regulation device, the output of the photovoltaic inverter in the distribution network can be controlled according to the optimal reactive power of the photovoltaic inverter, and the output of the static var compensator in the distribution network can be controlled according to the optimal reactive power of the static var compensator.

[0146] In this way, after determining the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator, the node voltage can be controlled by controlling the reactive power output of the photovoltaic inverter and the static var compensator, thereby controlling the active power of the feeder load and making the power of the downstream grid point reach the actual value of the optimal power of the downstream grid point.

[0147] In other words, since traditional slow-regulation devices such as capacitors and on-load tap-changing transformers have slow regulation speeds and cannot meet regulation requirements, the reactive power output of fast-regulation devices such as photovoltaic inverters and static var compensators is coordinated and controlled to control the active power of the feeder load, so that the actual power value of the downstream grid point is close to the power tracking value of the downstream grid point, and the feeder load participates in the power fluctuation smoothing of the downstream grid point of the distribution network, effectively realizing the smoothing of power fluctuation of the downstream grid point of the distribution network.

[0148] The distribution network power fluctuation mitigation method provided in this application pre-schedules slow control equipment based on source-load forecast values ​​during the day-ahead phase to obtain the optimal power at the distribution network point. During the intraday phase, the power tracking value at the distribution network point is determined based on the optimal power and the intraday power measurement value. Then, with the goal of making the actual intraday power value at the distribution network point as close as possible to the power tracking value, the optimal output of the fast control equipment is determined based on the power tracking value. The fast control equipment is then controlled according to the optimal output to control the active power of the feeder load, so that the feeder load participates in the power fluctuation mitigation of the distribution network point, thereby effectively achieving the mitigation of power fluctuations at the distribution network point.

[0149] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0150] Figure 3 This is a schematic diagram of the structure of a power fluctuation mitigation device for a distribution network point provided in an embodiment of this application. Figure 3 As shown, the power fluctuation suppression device for distribution network points provided in this embodiment may include: an acquisition module 301, a determination module 302, an acquisition module 303, and a control module 304.

[0151] The acquisition module 301 is used to acquire the source load prediction value of the day before, and solve the preset power optimization model based on the source load prediction value to obtain the optimal power of the downstream point of the day before. The power optimization model takes the sum of the difference between the estimated power of the downstream point corresponding to two adjacent cycles and the adjustment amount of the slow control equipment in the distribution network as the first objective function.

[0152] The determination module 302 is used to determine the power tracking value of the downstream network point based on the optimal power of the downstream network point and the power measurement value of the downstream network point during the day.

[0153] Module 303 is used to obtain the measured values ​​of source load during the day. Based on the power tracking value of the downstream network point and the measured values ​​of source load, the preset output optimization model is solved to obtain the optimal output of the fast control equipment in the distribution network. The output optimization model takes minimizing the difference between the power tracking value of the downstream network point and the actual power value of the downstream network point during the day as the second objective function.

[0154] The control module 304 is used to control the fast control device according to the optimal output of the fast control device, so as to control the active power of the feeder load and smooth the power fluctuation of the distribution network.

[0155] Optionally, the power optimization model uses the adjustment amount of the slow-regulating equipment as the decision variable, and the AC power flow constraint, the first node voltage constraint, the first active power balance constraint, the first downstream active power constraint, the capacitor operation constraint, the on-load tap-changing transformer operation constraint, and the first feeder load voltage-power coupling characteristic constraint as the first constraint conditions; wherein, the slow-regulating equipment includes capacitors and on-load tap-changing transformers in the distribution network; the acquisition module 301 is further used for:

[0156] Based on the predicted source load value, the first objective function of the power optimization model is solved according to the first constraint condition to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer.

[0157] The optimal power at the lower grid point is obtained based on the optimal switching state of the capacitor and the estimated power at the lower grid point corresponding to the optimal output voltage of the on-load tap-changing transformer.

[0158] Optionally, the determining module 302 is also used for:

[0159] Linear interpolation is performed on the optimal power and power measurement values ​​at the next grid point;

[0160] The power tracking value of the lower grid point is obtained based on the interpolation result.

[0161] Optionally, the output optimization model uses the output of the fast-regulating device as the decision variable, and the second node voltage constraint, the second active power balance constraint, the second downstream active power constraint, the photovoltaic inverter operation constraint, the static var compensator operation constraint, the voltage change constraint, and the second feeder load voltage-power coupling characteristic constraint as the second constraint conditions; wherein, the fast-regulating device includes the photovoltaic inverter and the static var compensator in the distribution network; the module 303 is further used for:

[0162] Based on the power tracking value at the lower grid point and the measured source load value, the second objective function of the output optimization model is solved according to the second constraint condition to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator.

[0163] Optionally, the control module 304 is also used for:

[0164] The output of the photovoltaic inverter in the distribution network is controlled according to the optimal reactive power of the photovoltaic inverter;

[0165] The output of the static var compensator in the distribution network is controlled according to the optimal reactive power of the static var compensator.

[0166] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0167] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0168] Those skilled in the art will recognize that the templates, units, and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0169] If the module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0170] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for smoothing power fluctuations at distribution network points, characterized in that, include: Obtain the predicted source load value for the day before, and solve the preset power optimization model based on the predicted source load value to obtain the optimal power of the next grid point for the day before. The power optimization model takes minimizing the difference in the estimated power of the downstream points corresponding to two adjacent cycles as the sum of the adjustment amounts of the slow control devices in the distribution network as its first objective function. The power tracking value of the downstream network point is determined based on the optimal power of the downstream network point and the power measurement value of the downstream network point within the day; The measured values ​​of source load within the day are obtained. Based on the power tracking value of the downstream network point and the measured values ​​of source load, the preset output optimization model is solved to obtain the optimal output of the fast control equipment in the distribution network. The output optimization model takes minimizing the difference between the power tracking value of the downstream network point and the actual power value of the downstream network point within the day as the second objective function. The fast control device is controlled according to its optimal output to control the active power of the feeder load and smooth out power fluctuations at distribution network points.

2. The method for smoothing power fluctuations at distribution network points according to claim 1, characterized in that, The power optimization model uses the adjustment amount of the slow-regulating equipment as the decision variable, and the first constraint conditions are AC power flow constraints, first node voltage constraints, first active power balance constraints, first downstream active power constraints, capacitor operation constraints, on-load tap-changing transformer operation constraints, and first feeder load voltage-power coupling characteristic constraints; wherein, the slow-regulating equipment includes capacitors and on-load tap-changing transformers in the distribution network. The step of solving the preset power optimization model based on the predicted source load value to obtain the optimal power of the downstream network point before the current day includes: Based on the predicted source load value, the first objective function of the power optimization model is solved according to the first constraint condition to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer. The optimal power at the lower grid point is obtained based on the optimal switching state of the capacitor and the estimated power at the lower grid point corresponding to the optimal output voltage of the on-load tap-changing transformer.

3. The method for smoothing power fluctuations at distribution network points according to claim 2, characterized in that, The expression for the first objective function is: In the formula, For the estimated power of the network points under period k, For the estimated power of the network points in period k-1, This represents the switching state of the capacitor at node i during cycle k; the capacitor is 1 when switched on and 0 when switched off. V0 is the rated voltage. Let N be the output voltage of the on-load tap-changing transformer at node i in period k; each period is 15 minutes, k∈T, where T is the time set, taking one day; α, β, and γ are weighting coefficients, and N is ... CB Let N be the set of nodes containing capacitors. OLTC This is a set of nodes containing on-load tap-changing transformers.

4. The method for smoothing power fluctuations at distribution network points according to any one of claims 1 to 3, characterized in that, The process of determining the power tracking value of the downstream network point based on the optimal power of the downstream network point and the power measurement value of the downstream network point within the day includes: Linear interpolation is performed on the optimal power and power measurement values ​​at the next grid point; The power tracking value of the lower grid point is obtained based on the interpolation result.

5. The method for smoothing power fluctuations at distribution network points according to claim 4, characterized in that, The expression for the power tracking value of the lower network point is: In the formula, P t pcc,tar The power tracking value of the network point during time period t. The measured power value at the network point under period k. The optimal power of the network point under period k; each time period is 1 minute, each period is 15 minutes, t∈k, k∈T, T is the time set, which is one day.

6. The method for smoothing power fluctuations at distribution network points according to any one of claims 1 to 3, characterized in that, The output optimization model uses the output of the fast-regulating equipment as the decision variable, and the second constraint conditions are the second node voltage constraint, the second active power balance constraint, the second downstream active power constraint, the photovoltaic inverter operation constraint, the static var compensator operation constraint, the voltage change constraint, and the second feeder load voltage-power coupling characteristic constraint; wherein, the fast-regulating equipment includes photovoltaic inverters and static var compensators in the distribution network; The step of solving the preset output optimization model based on the power tracking value of the downstream network point and the measured source load value to obtain the optimal output of the fast control equipment in the distribution network includes: Based on the power tracking value at the lower grid point and the measured source load value, the second objective function of the output optimization model is solved according to the second constraint condition to obtain the optimal reactive power of the photovoltaic inverter and the optimal reactive power of the static var compensator.

7. The method for smoothing power fluctuations at distribution network points according to claim 6, characterized in that, The expression for the second objective function is: In the formula, P t pcc,tar P represents the power tracking value at the network point during time period t. t pcc P represents the actual power value of the network point during time period t. t pcc The reactive power of the photovoltaic inverter and the static var compensator are determined; t∈h, h∈T, h is the optimization period, each time period is 1 minute, each optimization period is 15 minutes, and T is the time set, which is one day.

8. The method for smoothing power fluctuations at distribution network points according to claim 6, characterized in that, The step of controlling the fast control device according to the optimal output of the fast control device includes: The output of the photovoltaic inverter in the distribution network is controlled according to the optimal reactive power of the photovoltaic inverter; The output of the static var compensator in the distribution network is controlled according to the optimal reactive power of the static var compensator.

9. A device for smoothing power fluctuations at distribution network points, characterized in that, include: The acquisition module is used to acquire the source load prediction value of the day before, and solve the preset power optimization model based on the source load prediction value to obtain the optimal power of the downstream point of the day before; the power optimization model takes the sum of the difference between the predicted power of the downstream point corresponding to two adjacent cycles and the adjustment amount of the slow control equipment in the distribution network as the first objective function. The determination module is used to determine the power tracking value of the downstream network point based on the optimal power of the downstream network point and the power measurement value of the downstream network point within the day; The module is used to obtain the measured values ​​of source load within the day. Based on the power tracking value of the downstream network point and the measured value of source load, the preset output optimization model is solved to obtain the optimal output of the fast control equipment in the distribution network. The output optimization model takes minimizing the difference between the power tracking value of the downstream network point and the actual power value of the downstream network point within the day as the second objective function. The control module is used to control the fast control device according to the optimal output of the fast control device, so as to control the active power of the feeder load and smooth the power fluctuation of the distribution network.

10. The power fluctuation smoothing device for distribution network points according to claim 9, characterized in that, The power optimization model uses the adjustment amount of the slow-regulating equipment as the decision variable, and the first constraint conditions are AC power flow constraints, first node voltage constraints, first active power balance constraints, first downstream active power constraints, capacitor operation constraints, on-load tap-changing transformer operation constraints, and first feeder load voltage-power coupling characteristic constraints; wherein, the slow-regulating equipment includes capacitors and on-load tap-changing transformers in the distribution network. The acquisition module is further configured to solve the first objective function of the power optimization model based on the first constraint condition according to the source load prediction value, so as to obtain the optimal switching state of the capacitor and the optimal output voltage of the on-load tap-changing transformer. The optimal power at the lower grid point is obtained based on the optimal switching state of the capacitor and the estimated power at the lower grid point corresponding to the optimal output voltage of the on-load tap-changing transformer.

Images