A distribution network multi-reactive resource coordinated dispatching method, system, terminal and medium

Abstract

The present invention discloses a method, system, terminal and medium for coordinated dispatching of multiple reactive resources in a distribution network, and relates to the technical field of power dispatching. The key points of the technical solution are: simulating the travel behavior of electric vehicles; treating electric vehicle aggregators as a charging load and a continuous reactive compensation device to participate in grid dispatching; establishing a deterministic reactive optimization model for distribution networks with the participation of electric vehicles; converting the deterministic reactive optimization model for distribution networks according to scenario data to obtain a two-stage distributed robust reactive optimization model based on scenario clustering; constraining the probability deviation of each discrete scenario, and solving it using a column and constraint generation algorithm. Through scenario clustering, the present invention can effectively characterize the EV charging load fluctuations, EV reactive capacity fluctuations and conventional load fluctuations ultimately caused by various random factors, and solve the problem that large-scale electric vehicles participating in reactive optimization of distribution networks are difficult to estimate, control and coordinate.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and more specifically, to a method, system, terminal and medium for coordinated dispatching of multiple reactive resources in a distribution network. Background Art

[0002] In recent years, the number of electric vehicles (EVs) has grown rapidly. A large number of EVs are connected to the distribution network in an unordered manner, which seriously affects the safety and economy of the distribution network operation. With the emergence of vehicle to grid (V2G) technology and bidirectional four-quadrant chargers, EVs can participate in the reactive power optimization scheduling of the power grid through orderly charging and discharging and reactive power compensation, thereby alleviating the impact of EV access to the distribution network.

[0003] At present, existing studies have verified the feasibility of EV reactive power compensation through bidirectional chargers. By changing the parameters of the charger, such as the power factor and power factor angle, the reactive power compensation behavior of EV can be controlled. However, most existing studies simulate the charging behavior of EVs with probability density functions, while ignoring their traffic attributes and not considering the adjustable capacity of EVs participating in reactive power compensation. In addition, most studies on EVs participating in reactive power compensation are analyzed based on deterministic models, ignoring the changes in the adjustable reactive power capacity of EVs caused by the uncertainty of the electric vehicles themselves.

[0004] Therefore, how to study and design a coordinated dispatching method, system, terminal and medium for multiple reactive resources in distribution networks that can overcome the above-mentioned defects is a problem that we urgently need to solve. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the purpose of the present invention is to provide a method, system, terminal and medium for coordinated dispatching of multiple reactive resources in a distribution network. Through scenario clustering, the EV charging load fluctuations, EV reactive capacity fluctuations and conventional load fluctuations ultimately caused by various random factors can be effectively characterized, thereby solving the problems of large-scale electric vehicles participating in the reactive optimization of distribution networks that are difficult to estimate, control and coordinate.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] In a first aspect, a method for coordinated dispatching of multiple reactive resources in a distribution network is provided, comprising the following steps:

[0008] The architecture of coupling the actual traffic road network with the distribution network is adopted, and the travel parameters of electric vehicles including the travel chain, departure time, and initial state of charge are sampled through the Monte Carlo method to simulate the travel behavior of electric vehicles.

[0009] The electric vehicle aggregator is considered as a charging load and continuous reactive power compensation device participating in the grid dispatch. The total charging load of each aggregator is the sum of the charging power of all electric vehicles under the jurisdiction of the corresponding aggregator.

[0010] Considering the traditional reactive power compensation equipment, a deterministic reactive power optimization model of the distribution network with the participation of electric vehicles is established to coordinate and optimize the scheduling of various reactive power voltage regulation equipment and electric vehicles;

[0011] Construct scenario data of multiple electric vehicles and loads, transform the deterministic reactive power optimization model of the distribution network according to the scenario data, and obtain a two-stage distributed robust reactive power optimization model based on scenario clustering;

[0012] The 1-norm and ∞-norm are used to constrain the probability deviation of each discrete scenario, and the column and constraint generation algorithm is used to solve the two-stage distributed robust reactive power optimization model based on scenario clustering to achieve coordinated dispatch of reactive resources in distribution networks.

[0013] Furthermore, the process of simulating the travel behavior of the electric vehicle is specifically as follows:

[0014] The two networks are coupled by using a one-to-one correspondence between road network nodes and distribution network nodes. Road network nodes are divided into residential areas, work areas and commercial areas according to their attributes.

[0015] The Weibull probability function is used to describe the travel time of electric vehicles and the length of stay in residential areas, and the generalized extreme value distribution is used to describe the length of stay of electric vehicles in work areas and commercial areas.

[0016] Considering that the speed of electric vehicles is affected by traffic flow and road conditions during driving, a speed and flow model is established;

[0017] The remaining state of charge (SOC) of the electric vehicle is calculated based on the normal distribution of the initial state of charge (SOC), and the charging time of the electric vehicle is calculated based on the remaining state of charge.

[0018] Furthermore, the calculation formula for dispatchable reactive capacity of electric vehicle aggregators as a continuous reactive compensation device participating in grid dispatch is as follows:

[0019] ;

[0020] in, represents the total reactive capacity of the jth aggregator at time t; represents the maximum reactive power of the mth vehicle under the jth aggregator at time t; Indicates j Total number of vehicles in EVA; express t Moment mThe vehicle's charging status is indicated by a 0-1 symbol, 1 for charging and 0 for not charging.

[0021] Furthermore, the distribution network deterministic reactive power optimization model takes minimizing network loss as an objective function;

[0022] And, the conventional reactive power compensation equipment includes a capacitor bank, a static reactive power generator and an on-load tap-changing transformer.

[0023] Furthermore, the process of the two-stage distributed robust reactive power optimization model based on scenario clustering is specifically as follows:

[0024] The conventional load time series probability model is used to describe the fluctuation of conventional load in each period;

[0025] The K-menas algorithm is used to cluster multiple sample data into multiple finite discrete scenarios, and the initial probability of each finite discrete scenario is obtained;

[0026] According to the flexible regulation characteristics of each reactive voltage regulating device, the discrete variables related to the CB switching state, OLTC gear capacitor bank switching state and on-load voltage regulator gear are set as the first-stage decision variables, and the remaining continuous variables are set as the second-stage variables. Combined with the established uncertain scenario set, a two-stage distributed robust reactive power optimization model based on scenario clustering is determined.

[0027] Furthermore, the two-stage distributed robust reactive power optimization model based on scenario clustering includes:

[0028] The objective function is expressed as:

[0029] ;

[0030] in, represents the first-stage decision variables; X represents the first-stage variable constraint set; Indicates in the scene P s The second-stage decision variables; Y represents the second-stage variable constraint set; is the actual probability of each scene The feasible domain of is the number of finite discrete scenarios; Represents a set of distribution network operation parameter vectors;

[0031] The first stage discrete variable constraints are expressed as:

[0032] ;

[0033] in, It represents the combination of OLTC's up / down gear 0-1 flag, gear adjustment times and CB's switching 0-1 flag, switching group number; It represents the maximum number of OLTC adjustments and the maximum number of CB input groups; It represents the product of each CB capacity and the number of connected groups, and the product of the OLTC gear and the corresponding voltage change; represents the set of CB input capacity and OLTC access node voltage;

[0034] The coupling constraint between the first stage and the second stage is expressed as:

[0035] ;

[0036] in, represents the set of products of the first-stage variables and the corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The set of products of the second-stage variables and the corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV and SVG; Indicates in the scene P s The set of voltage upper limits, current upper limits, and maximum output values ​​of OLTC, CB, EV, and SVG at each node in the lower distribution network; represents the set of products of the first-stage variables and the corresponding parameters in the equality constraints of the distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The set of products of the second-stage variables and the corresponding parameters in the equality constraints of the lower distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The active and reactive power injected into the lower distribution network and the input capacity of OLTC, CB, EV and SVG are aggregated;

[0037] The second stage continuous variable constraint is expressed as:

[0038] ;

[0039] in, Indicates in the scene P s The output of SVG and the reactive output of EV are combined; Indicates in the scene P s The output limit of SVG and the reactive output limit of EV are combined; Indicates in the scene P sThe product of the reactive power output of a single EV and the number of EVs; represents the set of total reactive output of EV;

[0040] The second-order cone constraint is expressed as:

[0041] ;

[0042] in, Indicates in the scene P s The reactive power injected into each point of the lower distribution network; Represents the active power set injected into each node of the distribution network; Indicates the distribution network line parameters; It represents the sum of squares of current and voltage in each branch of the distribution network.

[0043] Furthermore, the expression for constraining the probability deviation of each discrete scene using the 1-norm and the ∞-norm is specifically:

[0044] ;

[0045] in, and are the probability allowable deviation values ​​under 1-norm and ∞-norm constraints respectively; is the actual probability of each scene The feasible domain of Reason The vector composed of is the number of finite discrete scenarios.

[0046] In a second aspect, a distribution network multi-reactive resource coordinated dispatching system is provided, and the system is used to implement a distribution network multi-reactive resource coordinated dispatching method as described in any one of the first aspects, including:

[0047] The behavior simulation module is used to adopt the architecture of coupling the actual traffic network with the distribution network, and to sample the travel parameters of electric vehicles including the travel chain, departure time, and initial state of charge through the Monte Carlo method to simulate the travel behavior of electric vehicles;

[0048] A compensation analysis module is used to consider the electric vehicle aggregator as a charging load and a continuous reactive power compensation device to participate in the grid dispatch, and the total charging load of each aggregator is the sum of the charging powers of all electric vehicles under the jurisdiction of the corresponding aggregator;

[0049] The model building module is used to establish a deterministic reactive power optimization model of the distribution network with the participation of electric vehicles by considering traditional reactive power compensation equipment, so as to coordinate and optimize the scheduling of various reactive power voltage regulation equipment and electric vehicles;

[0050] The model conversion module is used to construct scenario data of multiple electric vehicles and loads, and convert the distribution network deterministic reactive power optimization model according to the scenario data to obtain a two-stage distributed robust reactive power optimization model based on scenario clustering;

[0051] The model solving module is used to constrain the probability deviation of each discrete scenario using the 1-norm and the ∞-norm, and to solve the two-stage distributed robust reactive power optimization model based on scenario clustering using the column and constraint generation algorithm to achieve coordinated dispatch of reactive power resources in the distribution network.

[0052] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for coordinated dispatching of multiple reactive resources in a distribution network as described in any one of the first aspects is implemented.

[0053] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement a method for coordinated scheduling of multiple reactive resources in a distribution network as described in any one of the first aspects.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The invention provides a method for coordinated dispatching of multiple reactive resources in a distribution network. By clustering scenarios, it can effectively characterize the EV charging load fluctuation, EV reactive capacity fluctuation and conventional load fluctuation ultimately caused by various random factors, solving the problem of difficulty in estimating, regulating and coordinating reactive optimization of distribution networks involving large-scale electric vehicles;

[0056] 2. The present invention shifts the charging load of EVs in time, which can effectively reduce the peak-to-valley difference of the charging load and reduce the network loss of the system;

[0057] 3. The present invention utilizes the EV idle period for reactive power compensation, which can further reduce the system network loss and alleviate the operating pressure of each reactive device in the system;

[0058] 4. The present invention adopts a reactive robust optimization model, which is more practical for regulating and controlling various reactive devices under uncertain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0060] Figure 1 This is a flow chart of Embodiment 1 of the present invention;

[0061] Figure 2This is a schematic diagram of a traffic network in Example 1 of the present invention;

[0062] Figure 3 This is a schematic diagram of a distribution network in Embodiment 1 of the present invention;

[0063] Figure 4 It is a schematic diagram of the comparison results of system load conditions after EVs participate in reactive power compensation at different penetration rates in Example 1 of the present invention;

[0064] Figure 5a Schematic diagram of SVG output in different cases in Example 1 of the present invention;

[0065] Figure 5b Schematic diagram of CB output at 32 nodes in different cases in Embodiment 1 of the present invention;

[0066] Figure 6a This is a clustering result diagram of conventional active load in Example 1 of the present invention;

[0067] Figure 6b This is a clustering result diagram of conventional reactive loads in Example 1 of the present invention;

[0068] Figure 6c This is a clustering result diagram of EV charging load in Example 1 of the present invention;

[0069] Figure 6d This is a clustering result diagram of EV reactive capacity in Example 1 of the present invention;

[0070] Figure 7 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0072] Embodiment 1: A method for coordinated dispatching of multiple reactive resources in a distribution network, such as Figure 1 As shown, the following steps are included:

[0073] S1: Using the architecture of coupling the actual traffic road network with the distribution network, the Monte Carlo method is used to sample the travel parameters of electric vehicles including the travel chain, departure time, and initial state of charge to simulate the travel behavior of electric vehicles;

[0074] S2: The electric vehicle aggregator is regarded as a charging load and continuous reactive power compensation device participating in the grid dispatch, and the total charging load of each aggregator is the sum of the charging power of all electric vehicles under the jurisdiction of the corresponding aggregator;

[0075] S3: Considering the traditional reactive power compensation equipment, a deterministic reactive power optimization model of the distribution network with the participation of electric vehicles is established to coordinate and optimize the scheduling of various reactive power voltage regulation equipment and electric vehicles;

[0076] S4: Construct scenario data of multiple electric vehicles and loads, transform the distribution network deterministic reactive power optimization model according to the scenario data, and obtain a two-stage distributed robust reactive power optimization model based on scenario clustering;

[0077] S5: The probability deviation of each discrete scenario is constrained by 1-norm and ∞-norm, and the column and constraint generation algorithm is used to solve the two-stage distributed robust reactive power optimization model based on scenario clustering to achieve coordinated dispatch of reactive resources in the distribution network.

[0078] In step S1 , the process of simulating the travel behavior of the electric vehicle is specifically implemented by steps S101 - S104 .

[0079] S101: The two networks are coupled in a one-to-one correspondence between road network nodes and distribution network nodes, that is, the charging location of the electric vehicle is a node in the road network, and the corresponding distribution network node is connected through the road network node. The road network nodes are divided into three types of nodes according to their attributes: residential areas (H), work areas (W) and commercial areas (C). Assuming that the starting and ending points of the EV's one-day trip are both residential area nodes, the travel chain is used to describe the travel route of the electric vehicle.

[0080] S102: A Weibull probability function with flexible fitting is used to describe the travel time of electric vehicles and the length of stay in residential areas, and a generalized extreme value distribution is used to describe the length of stay of electric vehicles in work areas and commercial areas.

[0081] S103: The speed of electric vehicles during driving is mainly affected by traffic flow and road conditions. The speed-flow model is introduced as follows:

[0082] ;

[0083] in, For road section The speed of the vehicle; For road section Length; For vehicles on the road The actual driving time; For vehicles on the road Zero flow travel time; For road section capacity of traffic; For road section Traffic volume; and are model parameters.

[0084] S104: The initial state of charge (SOC) of the electric vehicle satisfies the normal distribution, and the remaining state of charge (SOC) The solution is shown as follows:

[0085] ;

[0086] Where: is the initial state of charge SOC; is the driving distance; W is the battery capacity.

[0087] The charging time of electric vehicles The calculation formula can be expressed as:

[0088] ;

[0089] Where: For charging time; is the desired target SOC; is the charging power; For charging efficiency.

[0090] Step S2 is specifically implemented by steps S201-S203.

[0091] S201: The active discharge behavior of electric vehicles will consume the power battery, while the reactive power only flows between the charger and the power grid and will not affect the battery. The capacity of the charger is S, and the reactive power it can provide is The calculation formula is as follows:

[0092] ;

[0093] Where: is the charging power.

[0094] S202: The electric vehicle aggregator (EVA) is considered as a large charging load and continuous reactive power compensation device to participate in grid dispatch. The total charging load of each electric vehicle aggregator is the sum of the charging power of all electric vehicles under the jurisdiction of the aggregator, as follows:

[0095] ;

[0096] Where: is the charging load of the jth aggregator at time t; is the charging power of the mth vehicle under the jth aggregator at time t; is the charging status of the mth vehicle at time t (0-1), 1 for charging and 0 for not charging; is the total number of vehicles in the jth EVA;

[0097] S203: Considering that most of the loads in the power grid are inductive, electric vehicles are used for capacitive compensation. The calculation of the dispatchable reactive capacity of the aggregator is as follows:

[0098] ;

[0099] in, represents the total reactive capacity of the jth aggregator at time t; It represents the maximum reactive power of the mth vehicle under the jth aggregator at time t.

[0100] In this embodiment, the relevant parameter settings of EV are shown in Table 1-Table 2.

[0101] Table 1

[0102]

[0103] Table 2

[0104]

[0105] In step S3, the process of establishing the distribution network deterministic reactive power optimization model includes steps S301-S303.

[0106] S301: Taking the minimum network loss as the objective function, a distribution network deterministic reactive power optimization model is established:

[0107] ;

[0108] Where: T is the total number of time periods; i, j is the network node index; is the set of all nodes in the network; is the current of line ij at time t; is the resistance of line ij;

[0109] S302: Traditional reactive power compensation equipment such as capacitor banks (CB) and static VAR generators (SVG) are considered. In addition, an on-load tap changer (OLTC) is added to improve the overall voltage level of the system, and its constraints are as follows:

[0110] ;

[0111] ;

[0112] Where: represents the CB input capacity at node j at time t; is the number of CB input groups at node j at time t, which is a discrete variable; represents the number of CB input groups at node j at time t-1; is the capacity of each group of CBs at node j; and are the 0-1 identifiers of CB input and reduction at node j at time t, respectively; is the maximum number of CB switching times at node j during time period T; is the maximum number of CB input groups at node j; and are the upper and lower limits of the SVG reactive compensation power at node j respectively; represents the SVG input capacity at node j at time t; It is expressed as the square of the OLTC transformation ratio, defined as the ratio of the secondary side to the primary side, and is a discrete variable; Indicates the square of the upper limit of the OLTC transformation ratio; is the difference between the square of the OLTC gear ratio of gear z and gear z-1; It is the maximum adjustable gear; is a 0-1 indicator variable indicating that the OLTC at node j at time t is adjusted to gear z; It is represented as a 0-1 indicator variable indicating that the OLTC at node j at time t-1 is adjusted to the z gear; is a 0-1 indicator variable indicating that the OLTC at node j at time t is adjusted to the z-1 position; and They are the OLTC upshift and downshift 0-1 marks at node j at time t respectively; is the maximum adjustable number of OLTC at node j within the T period.

[0113] 303: Each node t Reactive power output of electric vehicle aggregators at the moment The upper limit is subject to the following constraints:

[0114] .

[0115] In this embodiment, OLTC is added to the first node, and the first node is regarded as a voltage adjustable point. The size of each CB group is 50kvar, and the specific parameter settings of each device are shown in Table 3.

[0116] Table 3

[0117]

[0118] In step S4, the process of establishing a two-stage distributed robust reactive power optimization model based on scenario clustering includes steps S401-S403.

[0119] S401: The conventional load time series probability model is used to describe the fluctuation of conventional load in each period, as shown in the following formula:

[0120] ;

[0121] Where: Indicates the basic active power of the distribution network; Indicates the basic reactive power of the distribution network; and are the expected values ​​of active power and reactive power of conventional load at time t respectively; and are the coefficients of variation of active power and reactive power of conventional load at time t respectively.

[0122] S402, M The sample data are clustered by K-menas method. finite discrete scenarios, and get the initial probability of each scenario .

[0123] S403, according to the flexible regulation characteristics of each reactive voltage regulating device, the CB switching state and OLTC gear position related discrete variables are set as the first stage decision variables , the remaining continuous variables are set as second-stage variables , combined with the established uncertain scenario set, the deterministic model can be expressed as the following compact uncertain form, including:

[0124] The objective function is expressed as:

[0125] ;

[0126] in, represents the first-stage decision variables; X represents the first-stage variable constraint set; Indicates in the scene P s The second-stage decision variables; Y represents the second-stage variable constraint set; is the actual probability of each scene The feasible domain of is the number of finite discrete scenarios; Represents a set of distribution network operation parameter vectors; min indicates the minimum value; max indicates the maximum value.

[0127] The first stage discrete variable constraints are expressed as:

[0128] ;

[0129] in, It represents the combination of OLTC's up / down gear 0-1 flag, gear adjustment times and CB's switching 0-1 flag, switching group number; It represents the maximum number of OLTC adjustments and the maximum number of CB input groups; It represents the product of each CB capacity and the number of connected groups, and the product of the OLTC gear and the corresponding voltage change; represents the set of CB input capacity and OLTC access node voltage;

[0130] The coupling constraint between the first stage and the second stage is expressed as:

[0131] ;

[0132] in, represents the set of products of the first-stage variables and the corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The set of products of the second-stage variables and the corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV and SVG; Indicates in the scene P s The set of voltage upper limits, current upper limits, and maximum output values ​​of OLTC, CB, EV, and SVG at each node in the lower distribution network; represents the set of products of the first-stage variables and the corresponding parameters in the equality constraints of the distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The set of products of the second-stage variables and the corresponding parameters in the equality constraints of the lower distribution network, OLTC, CB, EV, and SVG; Indicates in the scene P s The active and reactive power injected into the lower distribution network and the input capacity of OLTC, CB, EV and SVG are aggregated;

[0133] The second stage continuous variable constraint is expressed as:

[0134] ;

[0135] in, Indicates in the scene P s The output of SVG and the reactive output of EV are combined; Indicates in the scene P s The output limit of SVG and the reactive output limit of EV are combined; Indicates in the scene P s The product of the reactive power output of a single EV and the number of EVs; represents the set of total reactive output of EV;

[0136] The second-order cone constraint is expressed as:

[0137] ;

[0138] in, Indicates in the scene P s The reactive power injected into each point of the lower distribution network; Represents the active power set injected into each node of the distribution network; Indicates the distribution network line parameters; It represents the sum of squares of current and voltage in each branch of the distribution network.

[0139] In this embodiment, the load variation coefficient is 0.3. Assuming the number of EVs is 4400, the Monte Carlo method is used to simulate the travel behavior of EVs, and combined with the conventional load time series probability model, 10000 (M) scenario sample data are generated. Assuming the number of clusters of conventional load is 2, the number of clusters of EV charging load and reactive capacity is 5, and the two are combined to obtain 10 (Ns) typical scenarios.

[0140] In step S5, the process of realizing coordinated dispatch of reactive resources in distribution network includes steps S501-S502.

[0141] S501: The actual scenario probability distribution is different from the scenario probability obtained by clustering. The probability fluctuation of each scenario is used to reflect the uncertainty of electric vehicles and conventional loads. A confidence set based on 1-norm and ∞-norm is constructed to limit the probability fluctuation deviation of each scenario, as shown below:

[0142] ;

[0143] in, and are the probability allowable deviation values ​​under 1-norm and ∞-norm constraints respectively; is the actual probability of each scene The feasible domain of Reason The vector composed of is the number of finite discrete scenarios.

[0144] The following confidence level constraints are met:

[0145] ;

[0146] .

[0147] S502: Using the column and constraint generation algorithm, the problem is divided into a main problem and sub-problems for iterative solution, as shown in the following formula:

[0148] ;

[0149] ;

[0150] in, It represents the objective function value after the variables in the first stage are determined; m represents the mth iteration; n represents the maximum number of iterations.

[0151] In this embodiment, the following settings are made: Figure 2 The 32-node network shown is similar to Figure 3 A 33-node distribution system with a reference voltage of 12.66 kV and a reference capacity of 10 MVA is coupled to verify the effectiveness of the method proposed in the present invention. A day is divided into 24 hours and the simulation time interval is 1 hour.

[0152] The optimization effect of EV participating in active power transfer-reactive power compensation is analyzed under the deterministic model. Three cases are set up for comparative simulation: Case 1: EV disorderly charging; Case 2: EV participating in active power transfer-reactive power compensation at a 20% response rate; Case 3: EV participating in active power transfer-reactive power compensation at a 50% response rate.

[0153] The distribution of the total charging load of EVs over time under the three cases is as follows: Figure 4 shown.

[0154] from Figure 4 It can be seen that as the response rate of EVs participating in orderly charging increases, the peak charging load of EVs decreases from 2.5MW to 1.5MW, the distribution of charging load becomes more uniform, and the peak-to-valley difference decreases.

[0155] The comparison of the objective functions of each device without action and the objective functions of the three types of cases is shown in Table 4.

[0156] Table 4

[0157]

[0158] From the table above, it can be seen that after reactive power compensation, the system network loss has been significantly reduced. By comparing Case 1 with Cases 2 and 3, it can be seen that after considering EV participation in active power transfer-reactive power compensation, the system network loss has been further reduced, and the overall reactive power optimization effect is better.

[0159] Figure 5a and Figure 5bThe figure shows the output of various types of equipment after EV participates in reactive power compensation under different penetration rates. It can be seen that with the increase of the response rate of EV participation in optimized scheduling, the output of each device decreases to a certain extent, which effectively alleviates the operating pressure of each reactive power voltage regulation equipment.

[0160] For the analysis of uncertain scenarios, this embodiment analyzes Case 2 in which EVs participate in active power transfer-reactive power compensation at a 20% response rate.

[0161] Typical clustering results of EV and conventional load scenarios are shown in Figure 2. Figure 6a-6d After clustering the conventional active load and conventional reactive load, two results were obtained, namely category 1 and category 2, respectively. After clustering the EV charging load and EV reactive capacity, five results were obtained, namely category 1, category 2, category 3, category 4 and category 5.

[0162] Different confidence levels and This will result in different probability tolerance ranges and , which in turn affects the final optimization results. Table 5 shows the optimization results under different confidence levels. The value range is [0.2,0.99], The value range is [0.5,0.99].

[0163] Table 5

[0164]

[0165] It can be seen from the table that with and The network loss increases with the increase of . This is because as the confidence level increases, the confidence interval increases, the uncertainty contained in the model increases, and the final optimized solution is the expected value of the network loss under more severe conditions. In addition, it can be seen that when When the value is 0.5 and 0.8, the optimization results are almost unchanged. This shows that within a certain range, the optimization result is more obviously constrained by the ∞-norm.

[0166] Deterministic optimization is performed on 10,000 samples one by one, and the optimization results are compared with the solution results of the distributed robust optimization model proposed in the present invention, as shown in Table 6.

[0167] Table 6

[0168]

[0169] As can be seen from the table above, the optimization results of the distributed robust optimization model proposed in the present invention are smaller than those of the deterministic optimization model. This is because the model proposed in the present invention takes into account the fluctuations of EV and conventional loads, and takes into account certain adverse scenarios when optimizing the output decisions of each device, so that when the uncertainty fluctuations are large, large network losses will not occur. The deterministic model is optimized based on the predicted information, and has poor robustness for various uncertain scenarios.

[0170] Embodiment 2: A distribution network multi-reactive resource coordinated dispatching system, the system is used to implement a distribution network multi-reactive resource coordinated dispatching method as described in Embodiment 1, such as Figure 7 As shown, it includes a behavior simulation module, a compensation analysis module, a model building module, a model conversion module and a model solving module.

[0171] Among them, the behavior simulation module is used to adopt the architecture of coupling the actual traffic road network with the distribution network, and sample the travel parameters of electric vehicles including travel chains, departure time, and initial charge state through the Monte Carlo method to simulate the travel behavior of electric vehicles; the compensation analysis module is used to regard the electric vehicle aggregator as a charging load and continuous reactive compensation device to participate in the grid dispatch, and the total charging load of each aggregator is the sum of the charging power of all electric vehicles under the jurisdiction of the corresponding aggregator; the model construction module is used to consider the traditional reactive compensation equipment to establish a distribution network deterministic reactive optimization model with the participation of electric vehicles, so as to coordinate and optimize the dispatch of each reactive voltage regulating device and electric vehicles; the model conversion module is used to construct the scenario data of multiple electric vehicles and loads, and convert the distribution network deterministic reactive optimization model according to the scenario data to obtain a two-stage distributed robust reactive optimization model based on scenario clustering; the model solution module is used to constrain the probability deviation of each discrete scenario using the 1-norm and the ∞-norm, and solve the two-stage distributed robust reactive optimization model based on scenario clustering using the column and constraint generation algorithm to realize the coordinated dispatch of reactive resources in the distribution network.

[0172] The present invention also records a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for coordinated dispatching of multiple reactive resources in a distribution network as described in Example 1 is implemented.

[0173] The present invention also records a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement a method for coordinated scheduling of multiple reactive resources in a distribution network as described in Example 1.

[0174] Working principle: The coordinated dispatching method of multiple reactive resources in the distribution network under uncertain conditions with electric vehicles takes into account the traffic attributes of electric vehicles and the randomness of vehicle loads, clusters and characterizes the reactive capacity and different load sizes of EVs, and effectively reduces the peak-to-valley difference of charging load and alleviates the operating pressure of various reactive devices in the system by regulating the active power transfer-reactive power compensation of electric vehicles. In addition, through the reactive robust optimization model, the regulation of various reactive devices under uncertain conditions is more realistic.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for coordinated dispatching of multiple reactive resources in a distribution network, characterized in that: The following steps are involved: The architecture of coupling the actual traffic road network with the distribution network is adopted, and the travel parameters of electric vehicles including the travel chain, departure time, and initial state of charge are sampled through the Monte Carlo method to simulate the travel behavior of electric vehicles. The electric vehicle aggregator is considered as a charging load and continuous reactive power compensation device participating in the grid dispatch. The total charging load of each aggregator is the sum of the charging power of all electric vehicles under the jurisdiction of the corresponding aggregator. Considering the traditional reactive power compensation equipment, a deterministic reactive power optimization model of the distribution network with the participation of electric vehicles is established to coordinate and optimize the scheduling of various reactive power voltage regulation equipment and electric vehicles; Construct scenario data of multiple electric vehicles and loads, transform the deterministic reactive power optimization model of the distribution network according to the scenario data, and obtain a two-stage distributed robust reactive power optimization model based on scenario clustering; The 1-norm and ∞-norm are used to constrain the probability deviation of each discrete scenario, and the column and constraint generation algorithm is used to solve the two-stage distributed robust reactive power optimization model based on scenario clustering to achieve coordinated dispatch of reactive resources in distribution networks.

2. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 1, characterized in that: The process of simulating the travel behavior of electric vehicles is specifically as follows: The two networks are coupled by using a one-to-one correspondence between road network nodes and distribution network nodes. Road network nodes are divided into residential areas, work areas and commercial areas according to their attributes. The Weibull probability function is used to describe the travel time of electric vehicles and the length of stay in residential areas, and the generalized extreme value distribution is used to describe the length of stay of electric vehicles in work areas and commercial areas. Considering that the speed of electric vehicles is affected by traffic flow and road conditions during driving, a speed and flow model is established; The remaining state of charge (SOC) of the electric vehicle is calculated based on the normal distribution of the initial state of charge (SOC), and the charging time of the electric vehicle is calculated based on the remaining state of charge.

3. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 1, characterized in that: The calculation formula for dispatchable reactive capacity that considers the electric vehicle aggregator as a continuous reactive compensation device participating in grid dispatch is as follows: in, represents the total reactive capacity of the jth aggregator at time t; represents the maximum reactive power of the mth vehicle under the jth aggregator at time t; n j represents the total number of vehicles of the jth EV aggregator; τ j，m，t Indicates the charging status of the mth vehicle at time t (0-1), 1 for charging and 0 for not charging.

4. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 1, characterized in that: The distribution network deterministic reactive power optimization model takes minimizing network loss as the objective function; And, the conventional reactive power compensation equipment includes a capacitor bank, a static reactive power generator and an on-load tap-changing transformer.

5. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 1, characterized in that: The process of the two-stage distributed robust reactive power optimization model based on scenario clustering is specifically as follows: The conventional load time series probability model is used to describe the fluctuation of conventional load in each period; The K-menas algorithm is used to cluster multiple sample data into multiple finite discrete scenarios, and the initial probability of each finite discrete scenario is obtained; According to the flexible regulation characteristics of each reactive voltage regulating device, the discrete variables related to the CB switching state and OLTC gear position are set as the first-stage decision variables, and the other continuous variables are set as the second-stage variables. Combined with the established uncertain scenario set, a two-stage distributed robust reactive power optimization model based on scenario clustering is determined.

6. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 5, characterized in that: The two-stage distributed robust reactive power optimization model based on scenario clustering includes: The objective function is expressed as: Among them, x represents the first-stage decision variable; X represents the first-stage variable constraint set; y s In scene P s The second-stage decision variables; Y represents the second-stage variable constraint set; Ω p is the actual probability of each scene p s The feasible domain of N s is the number of finite discrete scenarios; A T Represents a set of distribution network operation parameter vectors; The first stage discrete variable constraints are expressed as: Wherein, Cx represents the set of OLTC gear up / down 0-1 flag, gear adjustment times, CB switching 0-1 flag, switching group number; c represents the set of OLTC maximum adjustment times and CB maximum number of switched groups; Dx represents the set of the product of each CB capacity and the number of switched groups and the product of OLTC gear and the corresponding voltage change; d represents the set of CB switched capacity and the voltage of the OLTC access node; The coupling constraint between the first stage and the second stage is expressed as: Where Gx represents the set of products of the first-stage variables and corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV and SVG; Hy s In scene P s The set of products of the second-stage variables and corresponding parameters in the inequality constraints of the distribution network, OLTC, CB, EV and SVG; g represents the product of the second-stage variables and corresponding parameters in the scenario P s The voltage upper limit, current upper limit of each node in the lower distribution network and the maximum output value of OLTC, CB, EV and SVG are set; Kx represents the product of the first-stage variables and corresponding parameters in the equality constraints of the distribution network, OLTC, CB, EV and SVG; My s In scene P s The set of products of the second-stage variables and corresponding parameters in the equality constraints of the distribution network, OLTC, CB, EV and SVG; u represents the product of the second-stage variables and corresponding parameters in the scenario P s The active and reactive power injected into the lower distribution network and the input capacity of OLTC, CB, EV and SVG are aggregated; The second stage continuous variable constraint is expressed as: Among them, Ey s In scene P s The output of SVG and the reactive output of EV are combined; e represents the output of SVG and the reactive output of EV in scenario P s The output limit of SVG and the reactive output limit of EV are combined; Fy s In scene P s The product of the reactive power output of a single EV and the number of EVs; f represents the total reactive power output of EVs; The second-order cone constraint is expressed as: ||Qy s +P||2≤q T y s +J; Among them, Qy s In scene P s The reactive power set injected into each point of the distribution network; P represents the active power set injected into each node of the distribution network; q T represents the line parameters of the distribution network; J represents the sum of the squares of the current and voltage of each branch of the distribution network.

7. A method for coordinated dispatching of multiple reactive resources in a distribution network according to claim 1, characterized in that: The expression for constraining the probability deviation of each discrete scene by using the 1-norm and the ∞-norm is specifically: Among them, θ1 and θ ∞ are the probability allowable deviation values ​​under 1-norm and ∞-norm constraints respectively; Ω p is the actual probability of each scene p s The feasible domain of p s The vector composed of s is the number of finite discrete scenarios, and p0 is the initial probability distribution of each scenario.

8. A distribution network multi-reactive resource coordinated dispatching system, characterized in that: The system is used to implement a distribution network multi-reactive resource coordinated dispatching method as described in any one of claims 1 to 7, comprising: The behavior simulation module is used to adopt the architecture of coupling the actual traffic network with the distribution network, and to sample the travel parameters of electric vehicles including the travel chain, departure time, and initial state of charge through the Monte Carlo method to simulate the travel behavior of electric vehicles; A compensation analysis module is used to consider the electric vehicle aggregator as a charging load and a continuous reactive power compensation device to participate in the grid dispatch, and the total charging load of each aggregator is the sum of the charging powers of all electric vehicles under the jurisdiction of the corresponding aggregator; The model building module is used to establish a deterministic reactive power optimization model of the distribution network with the participation of electric vehicles by considering traditional reactive power compensation equipment, so as to coordinate and optimize the scheduling of various reactive power voltage regulation equipment and electric vehicles; The model conversion module is used to construct scenario data of multiple electric vehicles and loads, and convert the distribution network deterministic reactive power optimization model according to the scenario data to obtain a two-stage distributed robust reactive power optimization model based on scenario clustering; The model solving module is used to constrain the probability deviation of each discrete scenario using the 1-norm and the ∞-norm, and to solve the two-stage distributed robust reactive power optimization model based on scenario clustering using the column and constraint generation algorithm to achieve coordinated dispatch of reactive power resources in the distribution network.

9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements a method for coordinated scheduling of multiple reactive resources in a distribution network as described in any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement a method for coordinated scheduling of multiple reactive resources in a distribution network as described in any one of claims 1 to 7.

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