Feeder voltage adjustable potential multi-target evaluation method and device considering flexible resource operation domain

Abstract

The invention discloses a feeder voltage adjustable potential multi-target evaluation method and device considering a flexible resource operation domain. The method comprises the following steps: constructing a flexible resource adjustment operation constraint model; establishing a deterministic model of node flexible resource operation domain evaluation considering the adjustment cost; in consideration of output of renewable energy sources in nodes and source load uncertainty of loads, a multi-scene stochastic optimization method is adopted to convert a deterministic model into a node flexible resource operation domain stochastic evaluation model, and upper and lower adjustable ranges of node power, baseline power and segmented unit adjustment cost after flexible resource aggregation are obtained; and finally, comprehensively considering feeder network constraint, adjustment cost after flexible resource aggregation and feeder source load prediction, and constructing a multi-target evaluation stochastic optimization model of the adjustable potential of the feeder voltage with minimum node voltage deviation and network power loss to obtain a quantized adjustable range of the feeder voltage.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution networks, and in particular relates to a multi-objective evaluation method and device for feeder voltage adjustable potential considering a flexible resource operation domain. Background Art

[0002] With the acceleration of global energy transformation, a large number of distributed generation (especially photovoltaic and wind power generation) have been incorporated into the distribution network feeders. Due to its volatility and intermittency, it has brought a series of new challenges to the safe and stable operation of the power system. Especially in terms of the voltage quality of the distribution network feeders, the widespread access of distributed generation will cause problems such as feeder voltage offset and voltage fluctuation, affecting the normal operation of the load. At the same time, the widespread access of flexible resources such as electric vehicles, energy storage devices, and controllable loads has transformed the distribution network from a network that only distributes electric energy to an integrated energy management system that integrates electric energy production, transmission, storage, and distribution. However, these resources have small capacity, high dispersion, and a wide variety of types, and it is very difficult to directly control these resources. Therefore, it is necessary to aggregate and manage a large number of flexible resources in the distribution network, so that they can participate in regulation and control, and make full use of the elasticity and flexibility of flexible resources to improve the feeder voltage quality.

[0003] In addition, reactive power compensation devices such as capacitors and static VAR generators are also an effective means to improve the voltage quality of distribution network feeders. Reasonable reactive power compensation after widespread access to distributed power generation can adjust the power flow distribution of the distribution network, improve voltage quality, and reduce active power loss.

[0004] In the above context, how to make full use of the active regulation potential of flexible resources in the distribution network and the reactive regulation capacity of reactive compensation devices, improve the absorption capacity of distributed renewable energy, and ensure the voltage quality of the distribution network during operation has become an important topic in the current power system research. By dispatching node flexible resources and reactive compensation devices, increasing the flexibility of feeder voltage regulation, it is possible to effectively alleviate the feeder voltage quality problems caused by the access of distributed generation, thereby ensuring the stable operation of the power grid. Summary of the invention

[0005] The purpose of the present invention is to propose a multi-objective evaluation method and device for feeder voltage adjustable potential considering the flexible resource operation domain, evaluate the flexible resource operation domain of each node, provide a multi-stage hierarchical framework to evaluate the feeder voltage adjustable potential to ensure the feeder voltage quality, and optimize the node voltage deviation and network power loss.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a multi-objective evaluation method for feeder voltage adjustable potential considering a flexible resource operation domain, comprising:

[0008] Collecting the regulation characteristics of flexible resources at each node in the feeder and establishing a flexible resource regulation operation constraint model; the flexible resources include distributed power sources, distributed energy storage and flexible loads;

[0009] Based on the flexible resource regulation operation constraint model, a deterministic model for evaluating the node flexible resource operation domain taking into account the regulation cost is established;

[0010] Considering the uncertainty of the source and load of renewable energy output and load in the node, based on the stochastic optimization method, the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is transformed into a stochastic evaluation model of the node flexible resource operation domain, and solved to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated;

[0011] Taking into account the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load prediction, a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential is constructed with the minimum node voltage deviation and network power loss. The model is solved to obtain the quantified feeder voltage adjustable range.

[0012] Preferably, the establishment of the flexible resource regulation operation constraint model includes:

[0013] The distributed power supply regulation operation constraint model is:

[0014] ;

[0015] in, is the index of the day-ahead scheduling period; For distributed power generation Active power output during the time period; The upper limit of the active power output of the distributed power source;

[0016] The distributed energy storage regulation operation constraint model is:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] in, and Energy storage Charging and discharging power during the time period; Indicates the maximum charging and discharging power of energy storage; For energy storage Binary charge / discharge decision for the time period, when 1 is taken, it is charging, and when 0 is taken, it is discharging; For energy storage The amount of stored electricity during the period; and Energy storage The charging and discharging efficiency of the time period; and are the minimum and maximum states of charge of energy storage, respectively; is the rated capacity of the energy storage; and are the stored electricity at the beginning and end of energy storage respectively; is the total operating cost of energy storage; is the unit operating cost of energy storage; is the time interval, is a collection of scheduling periods;

[0024] The flexible load regulation operation constraint model is:

[0025] ;

[0026] ;

[0027] ;

[0028] in, For flexible load The remaining cuttable power after time reduction; For flexible load Power reduction during the period; Total reduction cost for flexible loads; Reduce costs per unit of flexible load.

[0029] Preferably, the step of establishing a deterministic model for evaluating the node flexible resource operation domain taking into account the adjustment cost based on the flexible resource adjustment operation constraint model includes:

[0030] With the goal of minimizing the node operation cost, a deterministic model for evaluating the node flexible resource operation domain considering the adjustment cost is established as follows:

[0031] ;

[0032] in,

[0033] ;

[0034] ;

[0035] The deterministic model also needs to meet the following constraints:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] in, is the index of the node; is the index of the flexible resource on the corresponding node; For Node The total operating cost of energy storage; For Node Total reduction cost of flexible loads; For Node Penalty costs when active power cannot meet regulation requirements; For Node Providing benefits of regulating active power up and down; , They are The penalty coefficient of the upward and downward correction amount corresponding to the active power in the time period; , They are Time period node The upward and downward correction amounts when the active power regulation capability is insufficient; , They are The time period node provides the profit coefficient for increasing and decreasing the active power; , Respectively Time period node The amount of active power that can be provided for upward and downward regulation; It is a collection of distributed power sources, energy storage and flexible loads; represents the variable related to baseline power at the expected value; , Respectively represent the variables related to the upper and lower adjustable ranges of each node; represents the uncertainty variable; that is, , Respectively in Time period node The output active power of the distributed generation related to the upper baseline and the upper and lower adjustable ranges; , , Respectively in Time period node Flexible load curtailment power associated with baseline, up and down power ranges; , , Respectively in Time period node The energy storage discharge power associated with the baseline, up-regulated and down-regulated power ranges above; , , Respectively in Time period node Energy storage charging power associated with the baseline, up- and down-power ranges above; , Respectively in Time period node The load active power related to the upper and lower adjustable ranges; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upward power range is insufficient; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upper and lower power ranges is insufficient; for Time period node Baseline power on ; , Node lower and upper limits of baseline power; and Node The upper and lower limits of active power that can be provided; , For Node Upper limits of upward and downward correction amounts when active power regulation capability is insufficient.

[0043] Preferably, the source-load uncertainty of the renewable energy output and load in the node is considered, and the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is converted into a node flexible resource operation domain random evaluation model based on a stochastic optimization method, including:

[0044] The deterministic model of node flexible resource operation domain evaluation considering the adjustment cost is transformed into a random evaluation model of node flexible resource operation domain by using a multi-scenario random optimization method, as follows:

[0045] ;

[0046] ,

[0047] in, is the objective function of one stage; is a one-stage decision variable, including , , , , , , , ; is the objective function of the second stage; is a two-stage decision variable, including , , , , , , , , ; is the uncertainty variable, including , ; , are the index and collection of the sampling scenes respectively; is the probability of each scenario occurring, is the number of scenes.

[0048] Preferably, the uncertainty of the source and load of renewable energy output and load in the node is considered, and based on the stochastic optimization method, the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is converted into a random evaluation model of the node flexible resource operation domain, and solved to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated, including:

[0049] Solve the node flexible resource operation domain random evaluation model to obtain the node Power increase range , lower range and nodes Baseline power ,

[0050] The total adjustable power of a node is composed of the adjustable power of all distributed energy storage and flexible loads of the node, and the adjustable power of distributed energy storage and flexible loads depends on the operating status and power limit of the equipment at the previous moment;

[0051] Arrange each group of distributed energy storage and flexible loads with adjustable capacity in order from small to large according to their respective unit adjustment costs, and give priority to using flexible resources with low unit adjustment costs for adjustment until the adjustable power upper limit of the flexible resource is reached. At this stage, the aggregated unit adjustment cost is equal to the unit adjustment cost of the flexible resource;

[0052] When the upper limit of the adjustable power of the flexible resource is reached, the next flexible resource in the sorting will be used for adjustment. At this time, the aggregated unit adjustment cost is converted into the unit adjustment cost of this flexible resource, and finally the segmented unit adjustment cost after the flexible resource aggregation is obtained in a step-by-step manner.

[0053] Preferably, the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source load prediction are comprehensively considered to minimize the node voltage deviation and network power loss, and to construct a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential, including:

[0054] ;

[0055] in, ,

[0056] ,

[0057] ,

[0058] The multi-objective evaluation random optimization model must meet the constraints defined by the distributed power supply regulation operation model, and must also meet the following constraints:

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] in, is the weight factor, and ; For sampling scenes Lower node average voltage deviation; For branch A collection of; For sampling scenes Lower Branch Power loss; For Node A collection of; For sampling scenes Next Node exist The adjustment cost of flexible resources after aggregation in the time period; is the total number of network nodes; For Node Voltage; is the reference voltage of the root node; , Branch resistance and reactance; , Branch Active power and reactive power; For aggregated flexible resources Time period Unit adjustment cost of the segment; For Node After the flexible resources are aggregated, Time period Active power provided by the segment; It is segmented A collection of; is the index and set of capacitor bank units on a node; For Node Upper capacitor bank Units in Binary on / off decisions for time slots; For Node Upper capacitor bank The maximum number of switching changes allowed for each unit; for Time period node The reactive power output by the capacitor bank on is the reactive capacity of a single capacitor bank unit; , Respectively in Period through branch Active power and reactive power on For Node The parent node set of For Node The collection of child nodes; For Node The reactive power of the load at , are the lower and upper limits of the node voltage respectively; For branch The upper capacity limit.

[0069] Preferably, the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source load prediction are comprehensively considered, and a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential is constructed with the node voltage deviation and network power loss minimized, and the model is solved to obtain a quantified feeder voltage adjustable range, including:

[0070] Solving the multi-objective evaluation stochastic optimization model of the feeder voltage adjustable potential, the corresponding node average voltage deviation when considering the flexible resource operation domain can be obtained. ; Then, the power flow calculation is performed based on the expected value of each node load to obtain the corresponding node average voltage deviation in the absence of flexible resource response. The difference between the two is the adjustable range of feeder voltage deviation.

[0071] In a second aspect, the present invention provides a multi-objective evaluation device for feeder voltage adjustable potential considering a flexible resource operation domain, which is used to implement the multi-objective evaluation method for feeder voltage adjustable potential considering a flexible resource operation domain, and the device includes:

[0072] The first constraint module is used to collect the regulation characteristics of the flexible resources of each node in the feeder and establish a flexible resource regulation operation constraint model; the flexible resources include distributed power sources, distributed energy storage and flexible loads;

[0073] A first model module is used to establish a deterministic model for evaluating the node flexible resource operation domain taking into account the adjustment cost based on the flexible resource adjustment operation constraint model;

[0074] The second model module is used to consider the uncertainty of the source and load of renewable energy output and load in the node, and transform the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost into a random evaluation model of the node flexible resource operation domain based on the stochastic optimization method, and solve it to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated;

[0075] The quantitative evaluation module is used to comprehensively consider the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load prediction, and to construct a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential with the minimum node voltage deviation and network power loss, and solve it to obtain the quantified feeder voltage adjustable range.

[0076] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to perform any of the above-mentioned multi-objective evaluation methods for feeder voltage adjustable potential considering the flexible resource operation domain.

[0077] In a fourth aspect, the present invention provides a computing device comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above-mentioned multi-objective evaluation methods for feeder voltage adjustable potential considering the flexible resource operation domain.

[0078] The beneficial effects of the present invention are:

[0079] The present invention improves the flexibility of feeder voltage regulation by exploring the adjustable capacity of flexible resources and applying it to improve the feeder voltage regulation of the distribution network, thereby effectively alleviating the feeder voltage quality problems caused by the access of distributed power sources or new loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A schematic flow chart of a multi-objective evaluation method for feeder voltage adjustable potential considering a flexible resource operation domain provided by the present invention;

[0081] Figure 2 A distribution network topology diagram provided by an embodiment of the present invention;

[0082] Figure 3 The flexible resource operation domain of each node evaluated provided by the embodiment of the present invention;

[0083] Figure 4 A comparison diagram of node voltages before and after aggregation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0085] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0086] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0087] It should be emphasized here that the step marks mentioned below are not intended to limit the order of the steps, but it should be understood that the steps can be executed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be executed simultaneously.

[0088] Embodiment 1, as Figure 1 As shown, it is the first embodiment of the present invention. This embodiment 1 provides a multi-objective evaluation method for feeder voltage adjustable potential considering the flexible resource operation domain, including the following steps:

[0089] Step 1: Collect the regulation characteristics of flexible resources (including distributed power sources, distributed energy storage and flexible loads) at each node in the feeder, and establish the corresponding flexible resource regulation operation constraint model.

[0090] The flexible resource regulation and operation constraint model includes:

[0091] (a) Distributed power generation:

[0092] (1)

[0093] in, is the index of the day-ahead scheduling period; For distributed power generation Active power output during the time period; It is the upper limit of the active power output of distributed power generation.

[0094] (b) Distributed energy storage:

[0095] (2)

[0096] (3)

[0097] (4)

[0098] (5)

[0099] (6)

[0100] (7)

[0101] in, and Energy storage Charging and discharging power during the time period; Indicates the maximum charging and discharging power of energy storage; For energy storage Binary charge / discharge decision for the time period, when 1 is taken, it is charging, and when 0 is taken, it is discharging; For energy storage The amount of stored electricity during the period; and Energy storage The charging and discharging efficiency of the time period; and are the minimum and maximum states of charge of energy storage, respectively; is the rated capacity of the energy storage; and are the stored electricity at the beginning and end of energy storage respectively; is the total operating cost of energy storage; is the unit operating cost of energy storage; is the time interval, A collection of scheduling periods.

[0102] (c) Flexible load:

[0103] (8)

[0104] (9)

[0105] (10)

[0106] in, For flexible load The remaining cuttable power after time reduction; For flexible load Power reduction during the period; Total reduction cost for flexible loads; Reduce costs per unit of flexible load.

[0107] Step 2: Based on the flexible resource adjustment operation constraint model, a deterministic model for evaluating the node flexible resource operation domain considering the adjustment cost is established, as follows:

[0108] With the goal of minimizing node operation costs, a deterministic model for evaluating node flexible resource operation domain considering adjustment costs is established:

[0109] (11)

[0110] in,

[0111] (12)

[0112] (13)

[0113] The above objective function must satisfy the constraints (1)-(10) and the following constraints:

[0114] Node power balance constraints:

[0115] (14)

[0116] (15)

[0117] (16)

[0118] Inequality constraints:

[0119] (17)

[0120] (18)

[0121] (19)

[0122] in, is the index of the node; is the index of the flexible resource on the corresponding node; For Node The total operating cost of energy storage; For Node Total reduction cost of flexible loads; For Node Penalty costs when active power cannot meet regulation requirements; For Node Providing benefits of regulating active power up and down; , They are The penalty coefficient of the upward and downward correction amount corresponding to the active power in the time period; , They are Time period node The upward and downward correction amounts when the active power regulation capability is insufficient; , They are The time period node provides the profit coefficient for increasing and decreasing the active power; , Respectively Time period node The amount of active power that can be provided for upward and downward regulation; It is a collection of distributed power sources, energy storage and flexible loads; represents the variable related to baseline power at the expected value; , Respectively represent the variables related to the upper and lower adjustable ranges of each node; represents the uncertainty variable; that is, , Respectively in Time period node The output active power of the distributed generation related to the upper baseline and the upper and lower adjustable ranges; , , Respectively in Time period node Flexible load curtailment power associated with baseline, up and down power ranges; , , Respectively in Time period node The energy storage discharge power associated with the baseline, up-regulated and down-regulated power ranges above; , , Respectively in Time period node Energy storage charging power associated with the baseline, up- and down-power ranges above; , Respectively in Time period node The load active power related to the upper and lower adjustable ranges; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upward power range is insufficient; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upper and lower power ranges is insufficient; for Time period node Baseline power on ; , Node lower and upper limits of baseline power; and Node The upper and lower limits of active power that can be provided; , For Node Upper limits of upward and downward correction amounts when active power regulation capability is insufficient.

[0123] Step 3: Considering the uncertainty of renewable energy output and load in the node, the deterministic model of node flexible resource operation domain evaluation in step 2 is transformed into a random evaluation model of node flexible resource operation domain based on the stochastic optimization method to obtain the upper and lower adjustable ranges of node power, baseline power and the segmented unit adjustment cost after flexible resource aggregation; the details are as follows:

[0124] In order to deal with the uncertainty of renewable energy output and load in the node, a multi-scenario stochastic optimization method is used to solve it, that is, a large number of sampling scenarios are used in the prediction interval to effectively model the uncertainty. Since the probability of different scenarios is unknown, the sample average approximation method is used to assume that the probability of each scenario is ,in For the number of scenarios, a random evaluation model of node flexible resource operation domain is established:

[0125] (20)

[0126] st

[0127] ,

[0128] ,

[0129] in, is the objective function of one stage; is a one-stage decision variable, including , , , , , , , ; is the objective function of the second stage; is a two-stage decision variable, including , , , , , , , , ; is the uncertainty variable, including , ; , are the index and collection of the sampled scenes respectively.

[0130] Solve the above node flexible resource operation domain random evaluation model considering the adjustment cost and obtain the node Active power increase range boundary , lower the range boundary ,node Baseline power ,

[0131] The total adjustable amount of node power is composed of the adjustable amounts of all distributed energy storage and flexible loads of the node, and the adjustable amounts of distributed energy storage and flexible loads depend on the operating status and equipment power limit at the previous moment. Subsequently, considering the differences in the adjustable amount and unit adjustment cost of each group of distributed energy storage and flexible loads, according to the upper and lower adjustable ranges of node power, each group of distributed energy storage and flexible loads with adjustable amounts are arranged in order from small to large according to their respective unit adjustment costs, so as to obtain the segmented unit adjustment cost of the aggregated flexible resources. For example, assuming that the node contains only one group of adjustable distributed energy storage and flexible loads, and the unit adjustment cost of distributed energy storage is lower than that of flexible loads, from an economic perspective, distributed energy storage will be used for adjustment first until its adjustable power upper limit is reached. At this stage, the aggregated unit adjustment cost is equivalent to the unit adjustment cost of energy storage. When the adjustable power of energy storage reaches the upper limit, the remaining adjustment demand is borne by the flexible load. At this time, the aggregated unit adjustment cost is converted into the unit adjustment cost of the flexible load, thereby obtaining a step-by-step increasing unit adjustment cost.

[0132] Step 4: Comprehensively consider the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load forecast, and build a multi-objective evaluation model for the feeder voltage adjustable potential to minimize the node voltage deviation and network power loss, and quantify the feeder voltage adjustable range.

[0133] The multi-objective evaluation model for feeder voltage adjustable potential includes:

[0134] (twenty one)

[0135] st(1)

[0136] (twenty two)

[0137] (twenty three)

[0138] (twenty four)

[0139] (25)

[0140] (26)

[0141] (27)

[0142] (28)

[0143] (29)

[0144] (30)

[0145] (31)

[0146] (32)

[0147] in, is the weight factor, and ; is the average node voltage deviation; For branch A collection of; For branch Power loss; For Node A collection of; For Node exist The adjustment cost of flexible resources after aggregation in the time period; is the total number of network nodes; For Node Voltage; is the reference voltage of the root node; , Branch resistance and reactance; , Branch Active power and reactive power; For aggregated flexible resources Time period Unit adjustment cost of the segment; For Node After the flexible resources are aggregated, Time period Active power provided by the segment; It is segmented A collection of; is the index and set of capacitor bank units on a node; For Node Upper capacitor bank Units in Binary on / off decisions for time slots; For Node Upper capacitor bank The maximum number of switching changes allowed for each unit; for Time period node The reactive power output by the capacitor bank on is the reactive capacity of a single capacitor bank unit; , Respectively in Period through branch Active power and reactive power on For Node The parent node set of For Node The collection of child nodes; For Node The reactive power of the load at , are the lower and upper limits of the node voltage respectively; For branch The upper limit of the capacity. Formula (25) is used to limit the number of switching times of the capacitor bank; Formula (26) is used to calculate the output power of the capacitor bank; Formulas (27)-(29) are linear distribution network power flow models; Formula (30) is used to constrain the voltage of each node within the allowable range; Formula (31) indicates that the aggregated adjustable power of the flexible resources of each node is within the above adjustable range; Formula (32) is the branch power constraint.

[0148] Furthermore, the feeder source load prediction considered above is specifically as follows:

[0149] In order to deal with the uncertainty of feeder source load, a stochastic optimization method with sample average approximation is adopted to construct a multi-objective evaluation stochastic optimization model for feeder voltage adjustable potential:

[0150] (33)

[0151] st

[0152] ,

[0153] ,

[0154] in, is the number of scenes; , are the index and collection of the sampled scenes respectively.

[0155] , , Corresponding to the sampling scenes Next , , .

[0156] Solving the multi-objective evaluation stochastic optimization model for the above feeder voltage adjustable potential, the corresponding node average voltage deviation considering the flexible resource operation domain can be obtained. Then, the power flow calculation is performed based on the expected value of each node load to obtain the corresponding node average voltage deviation in the absence of flexible resource response. The difference between the two is the adjustable range of feeder voltage deviation.

[0157] The method of the present invention is described below with reference to specific implementation cases.

[0158] In order to verify the effectiveness of the above method of the present invention, this embodiment adopts the following Figure 2 The medium and low voltage distribution system shown in the figure is tested. The network has 33 nodes, 6 flexible resource operation domains, 4 capacitor banks, and 100 sets of renewable energy and load scenarios are generated to simulate uncertainties.

[0159] Figure 3 In this embodiment, the distributed power sources, distributed energy storage and flexible loads are aggregated by random optimization method, and the upward and downward adjustment and basic power trajectory of the distribution network flexible resources after aggregation are obtained. It can be seen that the flexible resources have a continuous downward adjustment of power from 7:00 and a continuous upward adjustment of power from 9:00.

[0160] In addition, in order to verify the reliability of the proposed method, this method is compared with another method as shown below.

[0161] Method 1: Each flexible resource is in a non-aggregated state and is adjusted to its own optimal economic efficiency.

[0162] The full-day operating cost of method 1 is 1734.867 yuan, and the full-day operating cost of the present invention is 2159.580 yuan.

[0163] Table 1 and Table 2 compare the average voltage deviation and network loss results of the present invention and method 1 in the time intervals of 11:00-12:00 and 18:00-19:00, respectively. Figure 4 The distribution of node voltages under different methods during the period of 11:00-12:00 is shown.

[0164] Table 1 Comparison results of different methods at 11:00-12:00

[0165]

[0166] Table 2 Comparison results of different methods at 18:00-19:00

[0167]

[0168] During the peak period of photovoltaic power generation at 12:00-13:00 and the peak period of load demand at 18:00-19:00, flexible resources have a large adjustable range and can fully adjust the network.

[0169] The method proposed in the present invention not only achieves the minimum average voltage deviation, but also effectively reduces the power loss of the system, verifies the multi-objective evaluation method of feeder voltage adjustable potential considering the flexible resource operation domain proposed in the present invention, and makes full use of the flexibility of flexible resources to reduce power loss and improve the efficiency of voltage quality.

[0170] Based on the same inventive concept, the present invention also provides a multi-objective evaluation device for feeder voltage adjustable potential considering a flexible resource operation domain, which is used to implement the multi-objective evaluation method for feeder voltage adjustable potential considering a flexible resource operation domain disclosed in the above embodiment, and the device includes:

[0171] The first constraint module is used to collect the regulation characteristics of the flexible resources of each node in the feeder and establish a flexible resource regulation operation constraint model; the flexible resources include distributed power sources, distributed energy storage and flexible loads;

[0172] A first model module is used to establish a deterministic model for evaluating the node flexible resource operation domain taking into account the adjustment cost based on the flexible resource adjustment operation constraint model;

[0173] The second model module is used to consider the uncertainty of the source and load of renewable energy output and load in the node, and transform the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost into a random evaluation model of the node flexible resource operation domain based on the stochastic optimization method, and solve it to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated;

[0174] The quantitative evaluation module is used to comprehensively consider the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load prediction, and to construct a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential with the minimum node voltage deviation and network power loss, and solve it to obtain the quantified feeder voltage adjustable range.

[0175] It is worth pointing out that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation methods of the above-mentioned method embodiments are all applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be repeated here.

[0176] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute a multi-objective evaluation method for feeder voltage adjustable potential considering a flexible resource operation domain disclosed in the above-mentioned embodiment.

[0177] Based on the same inventive concept, the present invention also provides a computing device, comprising one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the multi-objective evaluation method for feeder voltage adjustable potential considering the flexible resource operation domain disclosed in the above-mentioned embodiment.

[0178] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0179] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain, characterized in that: include: Collect the regulation characteristics of flexible resources at each node in the feeder and establish a flexible resource regulation operation constraint model; the flexible resources include distributed power sources, distributed energy storage and flexible loads; Based on the flexible resource regulation operation constraint model, a deterministic model for evaluating the node flexible resource operation domain taking into account the regulation cost is established; Considering the uncertainty of the source and load of renewable energy output and load in the node, based on the stochastic optimization method, the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is transformed into a stochastic evaluation model of the node flexible resource operation domain, and solved to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated; Taking into account the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load prediction, a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential is constructed with the minimum node voltage deviation and network power loss. The model is solved to obtain the quantified feeder voltage adjustable range.

2. According to claim 1, a multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain is characterized in that: The establishment of the flexible resource regulation operation constraint model includes: The distributed power supply regulation operation constraint model is: ； in, is the index of the day-ahead scheduling period; For distributed power generation Active power output during the time period; The upper limit of the active power output of the distributed power source; The distributed energy storage regulation operation constraint model is: ； ； ； ； ； ； in, and Energy storage Charging and discharging power during the time period; Indicates the maximum charging and discharging power of energy storage; For energy storage Binary charge / discharge decision for the time period, when 1 is taken, it is charging, and when 0 is taken, it is discharging; For energy storage The amount of stored electricity during the period; and Energy storage The charging and discharging efficiency of the time period; and are the minimum and maximum states of charge of energy storage, respectively; is the rated capacity of the energy storage; and are the stored electricity at the beginning and end of energy storage respectively; is the total operating cost of energy storage; is the unit operating cost of energy storage; is the time interval, is a collection of scheduling periods; The flexible load regulation operation constraint model is: ； ； ； in, For flexible load The remaining cuttable power after time period reduction; For flexible load Power reduction during the period; Total reduction cost for flexible loads; Reduce costs per unit of flexible load.

3. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain according to claim 2, characterized in that: The method of establishing a deterministic model for evaluating the node flexible resource operation domain taking into account the adjustment cost based on the flexible resource adjustment operation constraint model includes: With the goal of minimizing the node operation cost, a deterministic model for evaluating the node flexible resource operation domain considering the adjustment cost is established as follows: ； in, ； ； The deterministic model also needs to meet the following constraints: ； ； ； ； ； ； in, is the index of the node; is the index of the flexible resource on the corresponding node; For Node The total operating cost of energy storage; For Node Total reduction cost of flexible loads; For Node Penalty costs when active power cannot meet regulation requirements; For Node Providing benefits of regulating active power up and down; , They are The penalty coefficient of the upward and downward correction amount corresponding to the active power in the time period; , They are Time period node The upward and downward correction amounts when the active power regulation capability is insufficient; , They are The time period node provides the profit coefficient for increasing and decreasing the active power; , Respectively Time period node The amount of active power that can be provided for upward and downward regulation; It is a collection of distributed power sources, energy storage and flexible loads; represents the variable related to baseline power at the expected value; , Respectively represent the variables related to the upper and lower adjustable ranges of each node; represents the uncertainty variable; that is, , Respectively in Time period node The output active power of the distributed generation related to the upper baseline and the upper and lower adjustable ranges; , , Respectively in Time period node Flexible load curtailment power associated with baseline, up and down power ranges; , , Respectively in Time period node The energy storage discharge power associated with the baseline, up-regulated and down-regulated power ranges above; , , Respectively in Time period node Energy storage charging power associated with the baseline, up- and down-power ranges above; , Respectively in Time period node The load active power related to the upper and lower adjustable ranges; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upward power range is insufficient; , Respectively in Time period node The upward and downward correction amounts when the active power regulation capability related to the upper and lower power ranges is insufficient; for Time period node Baseline power on ; , Node lower and upper limits of baseline power; and Node The upper and lower limits of active power that can be provided; , For Node Upper limits of upward and downward correction amounts when active power regulation capability is insufficient.

4. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain according to claim 3, characterized in that: The source-load uncertainty of the renewable energy output and load in the node is considered, and based on the stochastic optimization method, the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is converted into a node flexible resource operation domain stochastic evaluation model, including: The deterministic model of node flexible resource operation domain evaluation considering the adjustment cost is transformed into a random evaluation model of node flexible resource operation domain by using a multi-scenario random optimization method, as follows: ； ， in, is the objective function of one stage; is a one-stage decision variable, including , , , , , , , ; is the objective function of the second stage; is a two-stage decision variable, including , , , , , , , , ; is the uncertainty variable, including , ; , are the index and collection of the sampling scenes respectively; is the probability of each scenario occurring, is the number of scenes.

5. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain according to claim 4, characterized in that: The uncertainty of the source and load of renewable energy output and load in the node is considered. Based on the stochastic optimization method, the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost is converted into a random evaluation model of the node flexible resource operation domain, and solved to obtain the upper and lower adjustable ranges of the node power, the baseline power and the segmented unit adjustment cost after the flexible resources are aggregated, including: Solve the node flexible resource operation domain random evaluation model to obtain the node Power increase range , lower range and nodes Baseline power , The total adjustable power of a node is composed of the adjustable power of all distributed energy storage and flexible loads of the node, and the adjustable power of distributed energy storage and flexible loads depends on the operating status and power limit of the equipment at the previous moment; Arrange each group of distributed energy storage and flexible loads with adjustable capacity in order from small to large according to their respective unit adjustment costs, and give priority to using flexible resources with low unit adjustment costs for adjustment until the adjustable power upper limit of the flexible resource is reached. At this stage, the aggregated unit adjustment cost is equal to the unit adjustment cost of the flexible resource; When the upper limit of the adjustable power of the flexible resource is reached, the next flexible resource in the sorting will be used for adjustment. At this time, the aggregated unit adjustment cost is converted into the unit adjustment cost of this flexible resource, and finally the segmented unit adjustment cost after the flexible resource aggregation is obtained in a step-by-step manner.

6. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain according to claim 5, characterized in that: The multi-objective evaluation stochastic optimization model for feeder voltage adjustable potential is constructed by comprehensively considering feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and feeder source load prediction, with the node voltage deviation and network power loss minimized, including: ； in, , ， ， The multi-objective evaluation random optimization model must meet the constraints defined by the distributed power supply regulation operation model, and must also meet the following constraints: ； ； ； ； ； ； ； ； ； in, is the weight factor, and ; For sampling scenes Lower node average voltage deviation; For branch A collection of; For sampling scenes Lower Branch Power loss; For Node A collection of; For sampling scenes Next Node exist The adjustment cost of flexible resources after aggregation in the time period; is the total number of network nodes; For Node Voltage; is the reference voltage of the root node; , Branch resistance and reactance; , Branch Active power and reactive power; For the aggregated flexible resources Time period Unit adjustment cost of the segment; For Node After the flexible resources are aggregated, Time period Active power provided by the segment; It is segmented A collection of; is the index and set of capacitor bank units on a node; For Node Upper capacitor bank Units in Binary on / off decisions for time slots; For Node Upper capacitor bank The maximum number of switching changes allowed for each unit; for Time period node The reactive power output by the capacitor bank on is the reactive capacity of a single capacitor bank unit; , Respectively in Period through branch Active power and reactive power on For Node The parent node set of For Node The collection of child nodes; For Node The reactive power of the load at , are the lower and upper limits of the node voltage respectively; For branch The upper capacity limit.

7. A multi-objective evaluation method for feeder voltage adjustable potential considering flexible resource operation domain according to claim 6, characterized in that: The feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source load prediction are comprehensively considered, and a multi-objective evaluation stochastic optimization model for feeder voltage adjustable potential is constructed with the node voltage deviation and network power loss minimized, and the model is solved to obtain a quantified feeder voltage adjustable range, including: Solving the multi-objective evaluation stochastic optimization model of the feeder voltage adjustable potential, the corresponding node average voltage deviation when considering the flexible resource operation domain can be obtained. ; Then, based on the expected value of each node load, the power flow calculation is performed to obtain the corresponding node average voltage deviation in the absence of flexible resource response. The difference between the two is the adjustable range of feeder voltage deviation.

8. A multi-objective evaluation device for feeder voltage adjustable potential considering flexible resource operation domain, characterized in that: The device is used to implement the multi-objective evaluation method for feeder voltage adjustable potential considering the flexible resource operation domain as described in any one of claims 1 to 7, and comprises: The first constraint module is used to collect the regulation characteristics of the flexible resources of each node in the feeder and establish a flexible resource regulation operation constraint model; the flexible resources include distributed power sources, distributed energy storage and flexible loads; A first model module is used to establish a deterministic model for evaluating the node flexible resource operation domain taking into account the adjustment cost based on the flexible resource adjustment operation constraint model; The second model module is used to consider the uncertainty of the source and load of renewable energy output and load in the node, and transform the deterministic model of node flexible resource operation domain evaluation considering the adjustment cost into a random evaluation model of node flexible resource operation domain based on the stochastic optimization method, and solve it to obtain the upper and lower adjustable ranges of node power, baseline power and segmented unit adjustment cost after flexible resource aggregation; The quantitative evaluation module is used to comprehensively consider the feeder network constraints, the segmented unit regulation cost after flexible resource aggregation, and the feeder source and load prediction, and to construct a multi-objective evaluation stochastic optimization model for the feeder voltage adjustable potential with the minimum node voltage deviation and network power loss, and solve it to obtain the quantified feeder voltage adjustable range.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the multi-objective evaluation methods for feeder voltage adjustable potential considering a flexible resource operation domain according to claims 1 to 7.

10. A computing device, characterized in that It includes one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the multi-objective evaluation methods for feeder voltage adjustable potential considering the flexible resource operation domain according to claims 1 to 7.

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