A method and device for multi-objective evaluation of feeder voltage adjustable potential considering flexible resource operation domain

Abstract

The application discloses a kind of considering flexible resource operation field's feeder voltage adjustable potential multi-objective evaluation method and device, comprising: constructing flexible resource regulation operation constraint model;Establish the certainty model of node flexible resource operation field evaluation considering regulation cost;Considering the output of renewable energy in node and the source-load uncertainty of load, the certainty model is converted into node flexible resource operation field random evaluation model using multi-scenario stochastic optimization method, the upper and lower adjustable range of node power, baseline power and the segmented unit regulation cost after flexible resource aggregation are obtained;Finally, considering feeder network constraint, the regulation cost after flexible resource aggregation and feeder source-load forecast, with the minimum node voltage deviation and network power loss, construct the multi-objective evaluation stochastic optimization model of feeder voltage adjustable potential, obtain the quantified feeder voltage adjustable range.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution networks, and particularly relates to a feeder voltage adjustable potential multi-objective evaluation method and device considering flexible resource operation domains. BACKGROUND

[0002] With the accelerated promotion of global energy transformation, a large number of distributed power generation (especially photovoltaic and wind power generation) is connected to the feeder of the power distribution network. Due to its volatility and intermittency, it brings a series of new challenges to the safe and stable operation of the power system. Especially in terms of the voltage quality of the power distribution network feeder, the wide access of distributed power generation will cause problems such as feeder voltage deviation and voltage fluctuation, affecting the normal work of the load. At the same time, the wide access of flexible resources such as electric vehicles, energy storage devices and controllable loads makes the power distribution network transform from a network only for power distribution into a comprehensive energy management system integrating power generation, transmission, storage and distribution. However, these resources have small capacity, high dispersion and various types, and it is very difficult to directly control these resources. Therefore, the large number of flexible resources in the power distribution network need to be aggregated and managed so that they can participate in regulation and control, and fully utilize the flexibility and flexibility of flexible resources to improve the voltage quality of the feeder.

[0003] In addition, capacitor, static var generator and other reactive power compensation devices are also an effective means to improve the voltage quality of the feeder of the power distribution network. After the wide access of distributed power generation, reasonable reactive power compensation can adjust the power flow distribution of the power distribution network, improve the voltage quality and reduce the active power loss.

[0004] Under the above background, how to fully utilize the active regulation potential of flexible resources and the reactive regulation ability of reactive power compensation devices in the power distribution network, improve the consumption capacity of distributed renewable energy, and ensure the voltage quality of the power distribution network during operation, has become an important research topic of the current power system. By dispatching the flexible resources and reactive power compensation devices of the nodes, the flexibility of feeder voltage regulation is increased, which can effectively alleviate the feeder voltage quality problems caused by the access of distributed power generation, thereby ensuring the stable operation of the power grid. SUMMARY

[0005] The application aims to provide a feeder voltage adjustable potential multi-objective evaluation method and device considering flexible resource operation domains, which evaluates the flexible resource operation domains of each node, provides a multi-stage hierarchical framework to evaluate the feeder voltage adjustable potential for ensuring the voltage quality of the feeder, and optimizes the node voltage deviation and network power loss.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the application is as follows:

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

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

[0009] based on the flexible resource adjustment operation constraint model, establishing a deterministic model of node flexible resource operation domain evaluation considering adjustment cost;

[0010] considering source-load uncertainty of renewable energy output and load in the node, converting the deterministic model of node flexible resource operation domain evaluation considering adjustment cost into a node flexible resource operation domain random evaluation model based on a stochastic optimization method, and solving to obtain an upper and lower adjustable range of node power, baseline power and segmented unit adjustment cost of flexible resource aggregation;

[0011] comprehensively considering feeder network constraints, segmented unit adjustment cost of flexible resource aggregation and feeder source-load prediction, constructing a multi-objective evaluation stochastic optimization model of feeder voltage adjustable potential with minimum node voltage deviation and network power loss, and solving to obtain a quantitative feeder voltage adjustable range.

[0012] Preferably, the establishment of the flexible resource adjustment operation constraint model comprises:

[0013] The distributed power source adjustment operation constraint model is:

[0014] ;

[0015] wherein, is an index of a day-ahead dispatch period; is an active power output of the distributed power source in the period; is an upper limit of the active power output of the distributed power source;

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

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] wherein, and respectively the charging and discharging power of the energy storage in the time interval; denotes the maximum charging and discharging power of the energy storage; is the binary charging / discharging decision of the energy storage in the time interval, taking the value 1 for charging and 0 for discharging; is the stored energy of the energy storage in the time interval; and are the charging and discharging efficiencies of the energy storage in the time interval; and are the minimum and maximum state of charge of the energy storage, respectively; is the rated capacity of the energy storage; and are the stored energy of the energy storage at the beginning and end, respectively; is the total operation cost of the energy storage; is the unit operation cost of the energy storage; is the time interval, is the set of dispatching time intervals;

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

[0025] ;

[0026] ;

[0027] ;

[0028] wherein, is the remaining curable power of the flexible load after curtailment in the time interval; is the curtailed power of the flexible load in the time interval; is the total curtailment cost of the flexible load; is the unit curtailment cost of the flexible load.

[0029] Preferably, based on the flexible resource regulation operation constraint model, a deterministic model of node flexible resource operation domain evaluation considering regulation cost is established, comprising:

[0030] A deterministic model of node flexible resource operation domain evaluation considering regulation cost is established with the objective of minimizing the operation cost of the node, as follows:

[0031] ;

[0032] wherein,

[0033] ;

[0034] ;

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

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] wherein, is the index of the node; is the index of the flexible resource on the corresponding node; is the total operating cost of the energy storage on the node ; is the total curtailment cost of the flexible load on the node ; is the penalty cost when the active power on the node cannot meet the regulation demand; is the benefit of providing upward and downward active power on the node ; , are the penalty coefficients of the upward and downward correction amounts of the active power corresponding to the period ; , are the upward and downward correction amounts of the active power regulation capability of the node in the period; , are the benefit coefficients of providing upward and downward active power on the node in the period ; , respectively represent the upward and downward active power that the node can provide in the period ; is the set of distributed power sources, energy storage and flexible loads; denotes the variable related to the baseline power under the expected value; , respectively represent the variables related to the upward and downward adjustable range of each node; denotes the uncertain variable; that is, , , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the output active power of the flexible load shedding power related to the baseline, the up-regulation and the down-regulation power range at the time period node , , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the output active power of the flexible load shedding power related to the baseline, the up-regulation and the down-regulation power range at the time period node , , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the output active power of the flexible load shedding power related to the baseline, the up-regulation and the down-regulation power range at the time period node , , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the output active power of the flexible load shedding power related to the baseline, the up-regulation and the down-regulation power range at the time period node , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the output active power of the flexible load shedding power related to the baseline, the up-regulation and the down-regulation power range at the time period node , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the up-regulation and the down-regulation correction amount when the up-regulation power range related to the active power regulation capacity at the time period node , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node time period node the up-regulation and the down-regulation correction amount when the down-regulation power range related to the active power regulation capacity at the time period node is the baseline power at the time period node time period node is the baseline power at the time period node , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node the lower limit and the upper limit of the baseline power at the time period node and respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node the up-regulation and the down-regulation active power upper limit that can be provided at the time period node , respectively, the output active power of the distributed power source related to the baseline and the up-and-down adjustable range at the time period node the up-regulation and the down-regulation correction amount upper limit when the active power regulation capacity at the time period node

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

[0044] 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 by using a multi-scenario random optimization method, as follows:

[0045]

[0046]

[0047] wherein, is a one-stage objective function; is a one-stage decision variable, including is a two-stage objective function; is a two-stage decision variable, including is an uncertainty variable, including are an index and a set of sampling scenarios, respectively; is a probability of occurrence of each scenario, is a number of scenarios.

[0048] Preferably, the source-load uncertainty considering the renewable energy output and load in the node is based on a random optimization method to convert the deterministic model of the node flexible resource operation domain evaluation considering the adjustment cost into a node flexible resource operation domain random evaluation model, and to solve, to obtain an upper and lower adjustable range of node power, a baseline power, and a segmented unit adjustment cost after aggregation of flexible resources, including:

[0049] Solving the node flexible resource operation domain random evaluation model, an upper adjustable range of the node power , a lower adjustable range of the node power , and a baseline power of the node are obtained.

[0050] ​​​​​​​​​​​​​​​​​​​​​The total adjustable amount of node power is composed of the adjustable amount of all distributed energy storage and flexible load, and the adjustable amount of distributed energy storage and flexible load depends on the running state and equipment power limit of the previous time;

[0051] Each group of distributed energy storage and flexible load with adjustable amount is arranged in order according to the respective unit adjustment cost from small to large, and the flexible resource with low unit adjustment cost is preferentially used for adjustment until the adjustable power upper limit of the flexible resource is reached, and in this stage, the aggregated unit adjustment cost is equivalent to the unit adjustment cost of the flexible resource;

[0052] When the adjustable power upper limit of the flexible resource is reached, the next flexible resource in the order is adjusted, and at this time the aggregated unit adjustment cost is changed to the unit adjustment cost of the flexible resource, and finally the segmented unit adjustment cost of the aggregated flexible resource in a ladder-like increasing manner is obtained.

[0053] Preferably, the multi-objective evaluation random optimization model of the feeder voltage adjustable potential is constructed by comprehensively considering the feeder network constraint, the segmented unit adjustment cost of the aggregated flexible resource and the feeder source load prediction, and minimizing the node voltage deviation and network power loss, and includes:

[0054] ;

[0055] Among them, ,

[0056] ,

[0057] ,

[0058] The multi-objective evaluation random optimization model needs to meet the constraint conditions defined by the distributed power adjustment operation model, and also needs to meet the following constraint conditions:

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] wherein, is a weight factor, and ; is a sampled scenario of the average voltage deviation of the lower nodes; is a set of branches ; is a sampled scenario of the power loss of the branches ; is a set of nodes ; is a sampled scenario of the aggregated post-flexibility resource adjustment cost of the nodes over the time period ; is the total number of network nodes; is the voltage of the node ; is the reference voltage of the root node; , are the resistance and reactance of the branch , respectively; , are the active and reactive power of the branch , respectively; is the unit adjustment cost of the aggregated post-flexibility resource over the th segment of the time period ; is the active power provided by the aggregated post-flexibility resource of the node over the th segment of the time period ; is a set of segments ; is an index and a set of capacitor bank units on a node; is the binary on / off decision of the th unit of the capacitor bank on the node over the time period ; is the maximum number of switching transitions allowed for the th unit of the capacitor bank on the node ; is the reactive power output of the capacitor bank on the node over the time period ; is the reactive power capacity of a single capacitor bank unit; , are the active and reactive power of the branch over the time period active power and reactive power on the node; a parent node set of the node ; a child node set of the node ; a load reactive power at the node ; , a lower limit and an upper limit of a node voltage respectively; an upper limit of a capacity of the branch .

[0069] Preferably, the comprehensive consideration of the feeder network constraint, the segmented unit regulation cost after the flexible resource aggregation, and the feeder source load prediction is used to minimize the node voltage deviation and the network power loss, a multi-objective evaluation random optimization model of the feeder voltage adjustable potential is constructed, and a solution is obtained to obtain a quantitative feeder voltage adjustable range, including:

[0070] Solving the multi-objective evaluation random optimization model of the feeder voltage adjustable potential can obtain the corresponding node average voltage deviation when the operation domain of the flexible resource is considered; then, based on the expected value of each node load, a power flow calculation is performed to obtain the corresponding node average voltage deviation without the response of the flexible resource, and the difference between the two is the adjustable range of the feeder voltage deviation.

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

[0072] A 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 of the node flexible resource operation domain evaluation considering the regulation cost based on the flexible resource regulation operation constraint model;

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

[0075] A quantitative evaluation module is configured to comprehensively consider the feeder network constraint, the sectional unit regulation cost after flexible resource aggregation, and the feeder source load prediction, to minimize the node voltage deviation and network power loss, to construct a multi-objective evaluation random optimization model of the feeder voltage adjustable potential, and to solve the model to obtain the quantitative feeder voltage adjustable range.

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

[0077] In a fourth aspect, the present application 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 configured to be executed by the one or more processors, and the one or more programs comprise instructions for performing any of the methods of multi-objective evaluation of feeder voltage adjustable potential considering flexible resource operation domain according to the above.

[0078] The present application has the following beneficial effects:

[0079] The present application improves the flexibility of feeder voltage regulation by exploiting 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 types of loads. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 A flowchart of the method of multi-objective evaluation of feeder voltage adjustable potential considering flexible resource operation domain provided by the present application is shown.

[0081] Figure 2 A distribution network topology provided by an embodiment of the present application is shown.

[0082] Figure 3 The evaluated flexible resource operation domain of each node provided by an embodiment of the present application is shown.

[0083] Figure 4 A comparison chart of node voltages before and after aggregation provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0084] To make the objectives, technical solutions, and advantages of the present application clearer, the present application is further described in detail below with reference to the embodiments and drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not to limit the present application.

[0085] It is also necessary to point out that, in order to avoid obscuring the present application with unnecessary details, only the structures and / or processing steps closely related to the solution according to the present application are shown in the attached drawings, while other details that are not relevant to the present application are omitted.

[0086] It should be emphasized that the term "comprises / comprising" when used in this text refers to the presence of the 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 is important to note here that the step references mentioned in the following do not constitute a limitation of the order of the steps, but it should be understood that the steps can be performed in the order mentioned in the examples, differently from the order in the examples, or that several steps are performed simultaneously.

[0088] Example 1, as shown in Figure 1, is a first embodiment of the present application, which provides a method for multi-objective evaluation of the voltage adjustable potential of a feeder considering the flexible resource operating domain, comprising the following steps: Figure 1

[0089] Step 1, collect the adjustment characteristics of the flexible resources (including distributed power, distributed energy storage and flexible load) in the feeder, and establish the corresponding flexible resource adjustment operation constraint model,

[0090] The flexible resource adjustment operation constraint model includes:

[0091] (a) Distributed power:

[0092] (1)

[0093] wherein, is the index of the day-ahead dispatch period; is the active power output of the distributed power in the period; is the upper limit of the active power output of the distributed power.

[0094] (b) Distributed energy storage:

[0095] (2)

[0096] (3)

[0097] (4)

[0098] (5)

[0099] (6)

[0100] ​ (7)

[0101] where, and are the charging and discharging power of the energy storage in the time interval ; represents the maximum charging and discharging power of the energy storage; is the binary charging / discharging decision of the energy storage in the time interval , taking 1 for charging and 0 for discharging; is the storage power of the energy storage in the time interval ; and are the charging and discharging efficiency of the energy storage in the time interval ; and are the minimum and maximum state of charge of the energy storage, respectively; is the rated capacity of the energy storage; and are the initial and final storage power of the energy storage, respectively; is the total operation cost of the energy storage; is the unit operation cost of the energy storage; is the time interval, is the set of scheduling time intervals.

[0102] (c) Flexible load:

[0103] (8)

[0104] (9)

[0105] (10)

[0106] where, is the remaining available curtailed power of the flexible load after curtailment in the time interval ; is the curtailed power of the flexible load in the time interval ; is the total curtailment cost of the flexible load; is the unit curtailment cost of the flexible load.

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

[0108] A deterministic model for evaluating the node flexible resource operation domain considering adjustment cost is established with the goal of minimizing the operation cost of the node:

[0109] (11)

[0110] in,

[0111] (12)

[0112] (13)

[0113] The objective function described above must satisfy the constraints in (1) to (10), and also 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, For the index of the node; This serves as an index for the flexible resources on the corresponding node. For nodes Total operating cost of energy storage; For nodes Total cost reduction for flexible loads; For nodes The penalty cost when active power cannot meet regulation requirements; For nodes It provides benefits from both increasing and decreasing active power; , They are respectively Penalty coefficients for upward and downward adjustments to active power during a given time period; , They are respectively Time period nodes The upward and downward adjustment amounts when the active power regulation capability is insufficient; , They are respectively The time period provides the benefit coefficient for adjusting active power upwards and downwards; , They represent Period Node Up and down active power that can be provided; For the collection of distributed generation, energy storage and flexible load; Variable related to baseline power under expected value; 、 Variable related to up and down adjustable range of each node, respectively; Variable representing uncertainty; that is, 、 The output active power of the distributed power related to the baseline and the up and down adjustable range, respectively; Period Node The up and down adjustable range related to the baseline, up and down power range, respectively; 、 、 The output active power of the distributed power related to the baseline, up and down power range, respectively; Period Node The flexible load shedding power related to the baseline, up and down power range, respectively; 、 、 The energy storage discharge power related to the baseline, up and down power range, respectively; Period Node The energy storage charging power related to the baseline, up and down power range, respectively; 、 、 The active power of the load related to the baseline, up and down adjustable range, respectively; Period Node The up and down adjustable range related to the baseline, up and down power range, respectively; 、 The up and down adjustable range related to the baseline, up and down power range, respectively; Period Node The up and down adjustable range related to the baseline, up and down power range, respectively; 、 The up and down adjustable range related to the baseline, up and down power range, respectively; Period Node The up and down adjustable range related to the baseline, up and down power range, respectively; 、 The up and down adjustable range related to the baseline, up and down power range, respectively; Period Node The up and down adjustable range related to the baseline, up and down power range, respectively; Baseline power at Period Node ; 、 Lower and upper limits of the baseline power of the node ; And The upper limit of the up and down active power that can be provided by the node ; 、 is a node upper limit of up-regulation and down-regulation correction amount when active power regulation capability is insufficient.

[0123] Step 3: considering the source-load uncertainty of renewable energy output and load in the node, the deterministic model of node flexible resource operation domain evaluation in step 2 is converted into a random evaluation model of node flexible resource operation domain based on a stochastic optimization method, and the upper and lower adjustable ranges of node power, baseline power and segmented unit regulation cost after aggregation of flexible resources are obtained; the specific steps are as follows:

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

[0125] (20)

[0126] s.t.

[0127] ,

[0128] ,

[0129] wherein, is a one-stage objective function; is a one-stage decision variable, including , , , , , , , ; is a two-stage objective function; is a two-stage decision variable, including , , , , , , , , ; is an uncertainty variable, including , ; , Index and set of sampling scenarios, respectively.

[0130] Solving the above node flexible resource operating domain stochastic evaluation model considering adjustment cost, the active power up-regulation range boundary , down-regulation range boundary , baseline power of the node , ,

[0131] The total adjustable amount of node power is composed of the adjustable amounts of all distributed energy storage and flexible load in the node, and the adjustable amounts of distributed energy storage and flexible load depend on the operating state and equipment power limit at the previous time. Subsequently, considering the differences in the adjustable amounts and unit adjustment cost of each group of distributed energy storage and flexible load, each group of distributed energy storage and flexible load with adjustable amount is sequentially arranged from small to large according to the unit adjustment cost, according to the up and down adjustable range of node power, so as to obtain the segmented unit adjustment cost of the aggregated flexible resource. For example, assuming that the node only contains one group of adjustable distributed energy storage and flexible load, and the unit adjustment cost of distributed energy storage is lower than that of flexible load, from the economic point of view, the distributed energy storage will be used for adjustment first, until its adjustable power upper limit, at this stage, the aggregated unit adjustment cost is equivalent to the unit adjustment cost of the energy storage. When the adjustable power of the energy storage reaches the upper limit, the remaining adjustment demand is borne by the flexible load, at this time the aggregated unit adjustment cost changes to the unit adjustment cost of the flexible load, thus the unit adjustment cost in a ladder-like increasing manner can be obtained.

[0132] Step 4: Considering the feeder network constraint, the segmented unit adjustment cost of the aggregated flexible resource and the feeder source load forecast, a multi-objective evaluation model of feeder voltage adjustable potential is constructed to minimize the node voltage deviation and network power loss, so as to quantify the feeder voltage adjustable range.

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

[0134] (21)

[0135] s.t. (1)

[0136] (22)

[0137] (23)

[0138] (24)

[0139] (25)

[0140] (26)

[0141] (27)

[0142] (28)

[0143] (29)

[0144] (30)

[0145] (31)

[0146] (32)

[0147] wherein, is a weight factor, and ; is the node average voltage deviation; is the set of branches ; is the power loss of branch ; is the set of nodes ; is the aggregated post-flexibility resource adjustment cost of node in time period ; is the total number of network nodes; is the voltage of node ; is the reference voltage of the root node; , are the resistance and reactance of branch , respectively; , are the active and reactive power of branch , respectively; is the unit adjustment cost of the aggregated post-flexibility resource in segment of time period ; is the active power provided by the aggregated post-flexibility resource of node in segment of time period ; is the set of segments ; is the index and set of capacitor bank units on a node; is the binary on / off decision of the th unit of the capacitor bank on node in time period ; For nodes Upper capacitor bank The maximum number of switching changes allowed per unit; for Time period nodes The reactive power output of the capacitor bank on the capacitor bank; This refers to the reactive power capacity of a single capacitor bank unit. , They are respectively in Time period through branch road Active power and reactive power; For nodes The set of parent nodes; For nodes The set of child nodes; For nodes reactive power of the load at the location; , These are the lower and upper limits of the node voltage, respectively. branch road The upper limit of capacity. Equation (25) is used to limit the number of switching of the capacitor bank; Equation (26) is used to calculate the output power of the capacitor bank; Equations (27)-(29) are linearized power flow models of the distribution network; Equation (30) is used to constrain the voltage of each node within the allowable range; Equation (31) indicates that the adjustable power of the flexible resource aggregation of each node is within the above adjustable range; Equation (32) is the branch power constraint.

[0148] Furthermore, the above consideration of feeder source load prediction specifically includes:

[0149] To address the uncertainty of feeder source load, a sample-averaged approximation stochastic optimization method is employed to construct a multi-objective stochastic optimization model for evaluating the adjustable potential of feeder voltage.

[0150] (33)

[0151] st

[0152] ,

[0153] ,

[0154] in, The number of scenes; , These are the index and set of the sampling scenarios, respectively.

[0155] , , Corresponding to sampling scenarios Below , 、 .

[0156] Solving the above multi-objective evaluation random optimization model of feeder voltage adjustable potential can obtain the corresponding node average voltage deviation considering the flexible resource operation domain . Then, based on the expected value of each node load, the corresponding node average voltage deviation without flexible resource response is obtained by power flow calculation, and the difference between the two is the adjustable range of feeder voltage deviation.

[0157] The method of the application will be described below in combination with a specific implementation case.

[0158] In order to verify the effectiveness of the above method of the application, this embodiment uses the medium and low voltage distribution system as shown in Figure 2 for testing. The network has 33 nodes, 6 flexible resource operation domains, 4 capacitor groups, and generates 100 groups of renewable energy and load scenarios for simulating uncertainty.

[0159] Figure 3 To aggregate distributed power supply, distributed energy storage and flexible load by random optimization method under this embodiment, the up-regulation, down-regulation and basic power trajectory of the aggregated flexible resources of the distribution network are obtained. It can be seen that the flexible resources appear continuous downward adjustment power from 7:00, and continuous upward adjustment power from 9:00.

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

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

[0162] The total operation cost of method 1 is 1734.867 yuan, and the total operation cost of the application is 2159.580 yuan.

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

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

[0165]

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

[0167]

[0168] In the photovoltaic power generation peak period 12:00-13:00, and the load demand peak period 18:00-19:00, the flexible resources have a larger adjustable interval, and the network can be fully adjusted.

[0169] The method provided by the application not only realizes the minimum average voltage offset, but also effectively reduces the power loss of the system, verifies the multi-objective evaluation method of the adjustable potential of the feeder voltage considering the operation domain of the flexible resource, and fully utilizes the flexibility of the flexible resource to reduce the power loss and improve the efficiency of the voltage quality.

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

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

[0172] The first model module is used to establish a deterministic model for evaluating the operation domain of the node flexible resource considering the adjustment cost based on the flexible resource adjustment operation constraint model;

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

[0174] The quantitative evaluation module is used to comprehensively consider the feeder network constraint, the segmented unit adjustment cost of the aggregated flexible resource and the feeder source-load forecast, construct a multi-objective evaluation random optimization model of the adjustable potential of the feeder voltage with the minimum node voltage deviation and network power loss, and solve to obtain the quantitative adjustable range of the feeder voltage.

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

[0176] Based on the same inventive concept, the present application also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions which, when executed by a computing device, cause the computing device to perform the method for multi-objective assessment of feeder voltage adjustable potential considering flexible resource operation domains disclosed in the above embodiments.

[0177] Based on the same inventive concept, the present application also provides a computing device comprising one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing the method for multi-objective assessment of feeder voltage adjustable potential considering flexible resource operation domains disclosed in the above embodiments.

[0178] Those skilled in the art understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[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 flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the computer or other programmable data processing apparatus produce the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0180] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0181] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0182] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

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

2. The method of claim 1, wherein, The establishment of the flexible resource regulation and operation constraint model includes: The distributed power source regulation and operation constraint model is as follows: ； wherein, is an index of a day-ahead dispatch period; is an active power output of the distributed power source in the period; is an upper limit of the active power output of the distributed power source; The distributed energy storage regulation and operation constraint model is as follows: ； ； ； ； ； ； wherein, and are the charging and discharging power of the energy storage at time interval; denotes the maximum charging and discharging power of the energy storage; is the state of charge of the energy storage at time interval; is the binary charging / discharging decision of the energy storage at time interval, taking value 1 for charging and 0 for discharging; and are the charging and discharging efficiency of the energy storage at time interval; and are the minimum and maximum state of charge of the energy storage; is the rated capacity of the energy storage; and are the state of charge of the energy storage at the beginning and end of the time interval; is the total operation cost of the energy storage; is the unit operation cost of the energy storage; is the time interval, is the set of scheduling time intervals; The flexible load regulation operation constraint model is as follows: ； ； ； wherein, is the total flexible load in the period after the reduction of the remaining available curtailed power; is the total flexible load in the period after the reduction of the power; is the total curtailment cost of the flexible load; is the unit curtailment cost of the flexible load.

3. The method of claim 2, wherein, The determination model for evaluating the node flexible resource operation domain, based on the aforementioned flexible resource adjustment and operation constraint model and considering adjustment costs, includes: With the goal of minimizing node operating costs, a deterministic model for evaluating the node flexibility resource operating domain, taking into account adjustment costs, is established as follows: ； in, ； ； The deterministic model must also satisfy the following constraints: ； ； ； ； ； ； in, For the index of the node; This serves as an index for the flexible resources on the corresponding node. For nodes Total operating cost of energy storage; For nodes Total cost reduction for flexible loads; For nodes The penalty cost when active power cannot meet regulation requirements; For nodes It provides benefits from both increasing and decreasing active power; , They are respectively Penalty coefficients for upward and downward adjustments to active power during a given time period; , They are respectively Time period nodes The upward and downward adjustment amounts when the active power regulation capability is insufficient; , They are respectively The time period provides the benefit coefficient for adjusting active power upwards and downwards; , They represent Time period nodes The active power that can be adjusted upwards and downwards; It is a collection of distributed power sources, energy storage, and flexible loads; This represents the variable related to baseline power at the expected value; , These represent the variables related to the upper and lower adjustable ranges of each node, respectively. Represents uncertain variables; that is , They are respectively in Time period nodes The output active power of the distributed power source is related to the baseline and the adjustable range. , , They are respectively in Time period nodes Flexible load power reduction related to the baseline, upward and downward power ranges; , , They are respectively in Time period nodes The energy storage discharge power related to the baseline, upward and downward power ranges; , , They are respectively in Time period nodes Energy storage charging power related to the baseline, upward and downward power range; , They are respectively in Time period nodes The active power of the load is related to the baseline and the adjustable range above and below; , They are respectively in Time period nodes The upward and downward adjustment correction amounts when the active power regulation capability related to the upward adjustment power range is insufficient; , They are respectively in Time period nodes The upward and downward adjustment correction amounts when the active power regulation capability related to the upward and downward adjustment power range is insufficient; for Time period nodes Baseline power; , They are nodes The lower and upper limits of baseline power; and They are nodes The upper and lower limits of active power that can be adjusted upwards and downwards; , For nodes The upper limit of the upward and downward adjustment amount when the active power regulation capability is insufficient.

4. The multi-objective evaluation method for the adjustable potential of feeder voltage considering the flexible resource operating domain, as described in claim 3, is characterized in that... The method considers the source-load uncertainty of renewable energy output and load in nodes, and transforms the deterministic model of node flexible resource operation domain assessment considering adjustment costs into a stochastic assessment model of node flexible resource operation domain based on stochastic optimization methods, including: The deterministic model for evaluating the node flexible resource operating domain, which considers adjustment costs, is transformed into a stochastic evaluation model for the node flexible resource operating domain using a multi-scenario stochastic optimization method, as follows: ； ， in, This is the objective function for the first stage; For one-stage decision variables, including , , , , , , , ; The objective function is for the two-stage process; For two-stage decision variables, including , , , , , , , , ; For uncertain variables, including , ; , These are the index and set of the sampling scenarios, respectively; For the probability of each scenario occurring, The number of scenes.

5. The multi-objective evaluation method for the adjustable potential of feeder voltage considering the flexible resource operating domain as described in claim 4, characterized in that, The method considers the source-load uncertainty of renewable energy output and load in nodes, and transforms the deterministic model of node flexible resource operation domain evaluation considering adjustment costs into a stochastic evaluation model of node flexible resource operation domain based on stochastic optimization methods. The solution is then used to obtain the upper and lower adjustable ranges of node power, baseline power, and segmented unit adjustment costs after flexible resource aggregation, including: Solve the stochastic evaluation model of the node's flexible resource operating domain to obtain the node. Power adjustment range , Scope of adjustment and nodes Baseline power , The total adjustable power of a node consists of the adjustable power of all distributed energy storage and flexible loads at that node, and the adjustable power of distributed energy storage and flexible loads depends on the operating status and equipment power limitations of the previous moment. Each group of distributed energy storage and flexible load with adjustable capacity is arranged in order from small to large according to their respective unit adjustment costs. The flexible resources with low unit adjustment costs are used for adjustment first until the adjustable power 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. Once the adjustable power limit of the flexible resources is reached, the next flexible resource in the sequence will be used for adjustment. At this point, the unit adjustment cost after aggregation becomes the unit adjustment cost of the current flexible resource, resulting in segmented unit adjustment costs after aggregation of flexible resources in a step-like manner.

6. The multi-objective evaluation method for the adjustable potential of feeder voltage considering the flexible resource operating domain, as described in claim 5, is characterized in that... Taking into account feeder network constraints, segmented unit adjustment costs after flexible resource aggregation, and feeder source load prediction, a multi-objective stochastic optimization model for evaluating the adjustable potential of feeder voltage is constructed to minimize node voltage deviation and network power loss. This model includes: ； in, , ， ， The multi-objective evaluation stochastic optimization model must satisfy the constraints defined by the distributed power source regulation and operation model, and also the following constraints: ； ； ； ； ； ； ； ； ； in, As a weighting factor, and ; For sampling scenarios Average voltage deviation at the next node; branch road A set; For sampling scenarios Lower branch road Power loss; For nodes A set; For sampling scenarios Next node exist The cost of adjusting flexible resources after time-period aggregation; This represents the total number of network nodes. For nodes The voltage; This is the reference voltage for the root node; , Branch roads Resistance and reactance; , Branch roads Active power and reactive power; To aggregate flexible resources in Time period The unit adjustment cost of the segment; For nodes After the flexible resources are aggregated, Time period The active power provided by the segment; It is segmented A set; This refers to the index and set of capacitor bank cells on a node. For nodes Upper capacitor bank Each unit in Binary on / off decisions for time periods; For nodes Upper capacitor bank The maximum number of switching changes allowed per unit; for Time period nodes The reactive power output of the capacitor bank on the capacitor bank; This refers to the reactive power capacity of a single capacitor bank unit. , They are respectively in Time period through branch road Active power and reactive power; For nodes The set of parent nodes; For nodes The set of child nodes; For nodes reactive power of the load at the location; , These are the lower and upper limits of the node voltage, respectively. branch road The maximum capacity.

7. A multi-objective evaluation method for the adjustable potential of feeder voltage considering the flexible resource operating domain, as described in claim 6, is characterized in that... Taking into account feeder network constraints, segmented unit adjustment costs after flexible resource aggregation, and feeder source load prediction, a multi-objective stochastic optimization model for evaluating the adjustable potential of feeder voltage is constructed to minimize node voltage deviation and network power loss. The model is then solved to obtain a quantified adjustable range of feeder voltage, including: Solving the multi-objective stochastic optimization model for the adjustable feeder voltage potential yields the corresponding node average voltage deviation when considering the flexible resource operating domain. Subsequently, based on the expected load values ​​of each node, power flow calculations are performed to obtain the corresponding average node voltage deviation under the condition of no flexible resource response. The difference between the two is the adjustable range of the feeder voltage deviation.

8. A multi-objective evaluation device for the adjustable potential of feeder voltage considering the flexible resource operating domain, characterized in that, The apparatus for implementing the multi-objective evaluation method for feeder voltage adjustability potential considering flexible resource operating domains as described in any one of claims 1 to 7 includes: The first constraint module is used to collect the regulation characteristics of the flexible resources at each node in the feeder and establish a constraint model for the operation of the flexible resources; the flexible resources include distributed power sources, distributed energy storage and flexible loads. The 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 source-load uncertainty of renewable energy output and load in the node. Based on the stochastic optimization method, it transforms the deterministic model of the node flexible resource operation domain assessment considering adjustment costs into a stochastic assessment model of the node flexible resource operation domain, and solves it to obtain the upper and lower adjustable range of node power, baseline power, and segmented unit adjustment cost after flexible resource aggregation. The quantitative evaluation module is used to comprehensively consider feeder network constraints, segmented unit adjustment costs after flexible resource aggregation, and feeder source load prediction. It constructs a multi-objective evaluation stochastic optimization model for the adjustable potential of feeder voltage with the goal of minimizing node voltage deviation and network power loss, and solves it to obtain the quantified adjustable range of feeder voltage.

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

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