Collaborative Optimization Method and Device for Electric Vehicle Demand Response and Distribution Network Operation

Abstract

The present invention discloses a collaborative optimization method and device for electric vehicle demand response and distribution network operation. The method includes: Step 1, based on the electric vehicle user data in the target area, model the electric vehicle charging load and model it separately according to the classification of user charging habits; Step 2, according to the maintenance and operation characteristics of the distribution network, establish a mathematical model for the optimization of distribution network maintenance and operation on the basis of considering the maintenance cost; Step 3, according to the basic strategy of distribution network reconfiguration and the requirements of economy and security in the power system, establish a mathematical model for the optimization of distribution network reconfiguration operation; Step 4, aiming at minimizing the system network loss and the system maintenance cost, introduce the coupling constraints of maintenance and reconfiguration and the factors of electric vehicle charging load access, and establish an operation optimization model for maintenance collaborative distribution network reconfiguration considering the access of electric vehicles. The present invention provides a powerful analysis tool for the optimal economic operation of the distribution network containing electric vehicle charging load.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reconfiguration operation optimization, and particularly relates to a collaborative optimization method and device for electric vehicle demand response and distribution network operation. Background Art

[0002] Distribution network reconfiguration is an important means for optimizing the operation of a distribution system. Its essence is to optimize the current distribution network operation structure by changing the combined states of line tie switches and sectional switches under the condition of meeting the safe operation conditions such as system radiality, so as to achieve the purposes of reducing network losses and improving voltage quality.

[0003] Among various measures to reduce distribution network losses, distribution network reconfiguration has become the focus of research by various scholars because it does not require additional external investment. The distribution network is an intermediate link that receives electric energy from the transmission system or power plant and then distributes it to power users. It plays an important role in power supply to users. Different from the transmission network, its voltage level is relatively low, so the network losses are large. Therefore, the research on distribution network reconfiguration has important practical significance.

[0004] Regular maintenance of the power grid can effectively maintain its stable and safe operation. When arranging a maintenance plan for a line, the outage of the maintenance line can be analogized to the occurrence of a fault. Only at this time, the line outage is artificially caused and predictable, while the occurrence of a fault is an unpredictable and sudden line outage. The common point is that there is a line outage. Therefore, distribution network reconfiguration can be used for the distribution network maintenance plan arrangement, transferring the unmaintained load to a suitable branch, reducing the power outage area, and enabling the power grid to operate reasonably during the maintenance process.

[0005] In addition, under the background of energy conservation, emission reduction, carbon neutrality and carbon peak, governments of all countries are vigorously promoting and popularizing the use of new energy vehicles. Therefore, a large number of electric vehicle (EV) charging stations have been built around the world to meet the rapidly growing user group. However, the access of a large-scale electric vehicle charging load has a great negative impact on the power grid, making it necessary to further study the optimal operation of the distribution network.

[0006] From a new perspective, distribution network reconfiguration is no longer a single problem, but needs to be coordinated with electric vehicle charging and line maintenance as much as possible. Therefore, there is an urgent need for a maintenance collaborative distribution network reconfiguration operation optimization method considering the access of electric vehicles, so as to fully consider the factors of electric vehicle charging load and line maintenance, and provide a powerful analysis tool for the optimal economic operation of the distribution network containing electric vehicle charging load. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a method and device for optimizing the operation of a coordinated maintenance distribution network reconstruction considering the access of electric vehicles, which can fully consider the factors of electric vehicle charging load and line maintenance, and provide a powerful analysis tool for the optimal economic operation of the distribution network with electric vehicle charging load.

[0008] The first aspect of the embodiment of the present invention discloses a method for optimizing the coordination of electric vehicle demand response and distribution network operation, including:

[0009] Step 1, based on the electric vehicle user data of the target area, model the electric vehicle charging load, and model it separately for two scenario modes based on the user charging habits; the two scenario modes are "charging after the last driving" and "charging after each driving";

[0010] Step 2, according to the maintenance operation characteristics of the distribution network and on the basis of considering the maintenance cost, establish a mathematical model for optimizing the maintenance operation of the distribution network;

[0011] Step 3, according to the basic strategy of distribution network reconstruction and the requirements of economy and security in the power system, establish a mathematical model for optimizing the operation of distribution network reconstruction;

[0012] Step 4, aiming at the lowest system network loss and system maintenance cost, introduce the coupling constraints of maintenance and reconstruction and the factor of electric vehicle charging load access, and establish an optimized model for the coordinated maintenance distribution network reconstruction operation considering the access of electric vehicles.

[0013] As a preferred embodiment, in the first aspect of the present invention, the Step 1 includes:

[0014] Based on the electric vehicle user data of the target area, analyze the electric vehicle charging energy demand. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution;

[0015] Decompose the research on the charging power demand of electric vehicles into the research on the peak random characteristics of the charging power and the research on the shape of the charging power curve; the peak of the charging power is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape;

[0016] In the two scenario modes, fit the normalized charging power curve with a Gaussian function and record the peak characteristic value data of the charging power; the Gaussian function for fitting the normalized charging power curve is:

[0017]

[0018]

[0019] Wherein, P1 * (t) is the Gaussian function that fits the normalized charging power curve in the "charging after the last drive" scenario mode; P2 * (t) is the Gaussian function that fits the normalized charging power curve in the "charging after each drive" scenario mode; the values of the coefficients in the formula are respectively: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1.

[0020] As a preferred embodiment, in the first aspect of the present invention, the step 2 includes:

[0021] Considering the economic operation of the distribution network, the model objective function is set to the lowest system maintenance cost, and its specific functional formula is as follows:

[0022]

[0023] Wherein, N is the number of distribution network nodes, L is the set of distribution network lines, and T is the total number of time periods; H l,t is the maintenance cost of line l in time period t, and Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1;

[0024] The model maintenance constraints include line maintenance status variable constraints and line maintenance time variable constraints, and the specific constraint formulas are as follows:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] Wherein, MD L,l is the duration that line l needs to be maintained, NT is the total number of hours of the maintenance plan, and are the start time and end time of the maintenance; MT l on and MT l off are the shortest continuous duration and the minimum time interval of the maintenance section of line l respectively; p l,t and q l,t are the start index and end index of the maintenance respectively; p l,t = 1 indicates the start of the maintenance, otherwise p l,t = 0; q l,t = 1 indicates the end of the maintenance, otherwise q l,t = 0; τ is the maintenance time.

[0034] As a preferred embodiment, in the first aspect of the present invention, the step 3 includes:

[0035] Considering minimizing the network loss of the distribution network system, the selected model objective function is as follows:

[0036]

[0037] In the formula, N is the number of nodes in the distribution network, i is the node number, is the injection power of node i;

[0038] The relevant constraints of the distribution network reconfiguration model include node power balance, node voltage amplitude constraint, line current amplitude constraint, and distribution network radial topology constraint, which are specifically as follows:

[0039]

[0040]

[0041]

[0042]

[0043] β ij,t +β ji,t =α ij,t

[0044]

[0045]

[0046] 0≤α ij,t ≤1

[0047] In the formula, is the active load of node i in time period t, is the electric vehicle charging load of node i in time period t; is the injected reactive power of node i in time period t, is the reactive power load of node i at time period t; θ ij is the voltage phase difference, g ij and b ij are the conductance and susceptance of line ij in the π-type equivalent circuit respectively; V i,t and V j,t are the voltage amplitudes of node i and node j at time period t respectively; V i,max and V i,min are the upper and lower limits of the voltage amplitude of node i respectively; I ij,t is the current in line ij at time period t, I ijmax is the maximum allowable current in line ij, b sij is the susceptance to ground of line ij in the π-type equivalent circuit, U i,t and U j,t are the voltages of node i and node j at time period t respectively; β ij,t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij,t is the line connection status variable, also a binary variable, which is equal to 1 indicating that line ij is in the connected state at time period t and is equal to 0 indicating that line ij is disconnected at time period t, A ij 、B ij 、C ij 、D ij are all simplified parameters;

[0048] The distribution network reconfiguration problem is transformed from a difficult-to-solve mixed integer non-linear programming problem into an easy-to-solve mixed second-order cone programming problem through the second-order cone programming method. The relevant auxiliary variables and second-order cone constraint formulas are as follows:

[0049]

[0050]

[0051]

[0052]

[0053] In the formula, R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of node i and node j respectively. If α η =1, then there is If α η =0, then there is The same applies to node j.

[0054] As a preferred embodiment, in the first aspect of the present invention, step 4 includes:

[0055] Taking the optimal operation economy of the distribution network as the goal, the objective function is selected as the minimum sum of the maintenance cost of the distribution network system and the system network loss, specifically as follows:

[0056]

[0057] Based on the constraints in steps 2 and 3, the coupling constraints of distribution network maintenance and reconstruction are added as follows:

[0058]

[0059]

[0060]

[0061] In the formula, α l,t and α l,t-1 are line connection state variables, both of which are binary variables. When they are equal to 1, it means that line l is in the connected state in time period t or t - 1, and when they are 0, it means that line l is disconnected in time period t or t - 1;

[0062] Based on the electric vehicle charging load model established in step 1, electric vehicle charging pile loads are added to the original IEEE33 - node system, and simulation analysis is carried out in this system to verify the effectiveness of the model.

[0063] The second aspect of the present invention discloses a collaborative optimization device for electric vehicle demand response and distribution network operation, including:

[0064] A first modeling unit, configured to model the electric vehicle charging load according to the electric vehicle user data in the target area, and model it separately for two scenario modes based on the user's charging habits; the two scenario modes are "charging after the last driving" and "charging after each driving";

[0065] A second modeling unit, configured to establish a mathematical model for the optimal operation of distribution network maintenance according to the maintenance operation characteristics of the distribution network and on the basis of considering the maintenance cost;

[0066] A third modeling unit, configured to establish a mathematical model for the optimal operation of distribution network reconstruction according to the basic strategy of distribution network reconstruction and the requirements of economy and security in the power system;

[0067] The fourth modeling unit is used to establish an optimal operation model for coordinated maintenance and distribution network reconstruction considering the access of electric vehicles with the goal of minimizing the system power loss and system maintenance cost, introducing the coupling constraints of maintenance and reconstruction and the factors of access of electric vehicle charging loads.

[0068] As a preferred embodiment, in the second aspect of the present invention, the first modeling unit includes:

[0069] The demand analysis subunit is used to analyze the charging energy demand of electric vehicles according to the electric vehicle user data in the target area. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution;

[0070] The charging power research subunit is used to decompose the research on the charging power demand of electric vehicles into the research on the peak random characteristics of the charging power and the research on the shape of the charging power curve; the peak of the charging power is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape;

[0071] The fitting subunit is used to fit the normalized charging power curve with a Gaussian function in the two scenario modes respectively and record the peak characteristic value data of the charging power; the Gaussian function for fitting the normalized charging power curve is:

[0072]

[0073]

[0074] In the formula, P1 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after the last driving" scenario mode; P2 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after each driving" scenario mode; the values of each coefficient in the formula are: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1.

[0075] As a preferred embodiment, in the second aspect of the present invention, the second modeling unit includes:

[0076] The economic constraint subunit is used to consider the economic operation of the distribution network and set the model objective function to minimize the system maintenance cost. The specific function formula is as follows:

[0077]

[0078] In the formula, N is the number of nodes in the distribution network, L is the set of distribution network lines, T is the total number of time periods; Hl,t The maintenance cost of line l during time period t, Y l,t The line maintenance status of line l during time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1;

[0079] The model maintenance constraint sub-unit is used to perform constraints according to the line maintenance status variable constraint and the line maintenance time variable constraint included in the model maintenance constraint. The specific constraint equations are as follows:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, MD L,l is the duration for which line l needs to be maintained, NT is the total number of hours of the maintenance plan, and are the start time and end time of the maintenance respectively; MT l on and MT l off are the shortest duration and the minimum time interval of the line l maintenance segment respectively; p l,t and q l,t are the start index and end index of the maintenance respectively; p l,t =1 indicates the start of the maintenance, otherwise p l,t =0; q l,t =1 indicates the end of the maintenance, otherwise q l,t =0; τ is the maintenance time.

[0089] As a preferred embodiment, in the second aspect of the present invention, the third modeling unit includes:

[0090] The network loss constraint sub-unit is used to consider minimizing the network loss of the distribution network system, and the selected model objective function is as follows:

[0091]

[0092] Where \(N\) is the number of nodes in the distribution network, \(i\) is the node number, is the injection power of node \(i\);

[0093] The constraint sub-unit of the distribution network reconstruction model is used to perform constraints according to the relevant constraints of the distribution network reconstruction model, including node power balance, node voltage amplitude constraint, line current amplitude constraint, and radial topology constraint of the distribution network, as follows:

[0094]

[0095]

[0096]

[0097]

[0098] \(\beta\) ij,t +\(\beta\) ji,t =\(\alpha\) ij,t

[0099]

[0100]

[0101] 0\(\leq\)\(\alpha\) ij ,\(t\leq1\)

[0102] Where is the active load of node \(i\) at time period \(t\), is the electric vehicle charging load of node \(i\) at time period \(t\); is the injected reactive power of node \(i\) at time period \(t\), is the reactive load of node \(i\) at time period \(t\); \(\theta\) ij is the voltage phase difference, \(g\) ij and \(b\) ij are the conductance and susceptance of line \(ij\) in the \(\pi\)-type equivalent circuit respectively; \(V\) i,t and \(V\) j,t are the voltage amplitudes of node \(i\) and node \(j\) at time period \(t\) respectively; \(V\) i,max and \(V\) i,min are the upper and lower limits of the voltage amplitude of node \(i\) respectively; \(I\) ij ,\(t\) is the current in line \(ij\) at time period \(t\), \(I\) ijmax is the maximum allowable current in line \(ij\), \(b\) sij is the shunt susceptance of line \(ij\) to the ground in the \(\pi\)-type equivalent circuit, \(U\) i,t and \(U\) j,t are the voltages of node \(i\) and node \(j\) at time period \(t\) respectively; \(\beta\) ij, t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij , t is the line connection status variable, also a binary variable, which equals 1 indicating that line ij is in the connected state at time period t, and equals 0 indicating that line ij is disconnected at time period t, A ij , B ij , C ij , D ij are all simplified parameters;

[0103] The conversion subunit is used to convert the distribution network reconfiguration problem from a difficult-to-solve mixed-integer non-linear programming problem into an easy-to-solve mixed second-order cone programming problem through the second-order cone programming method. The relevant auxiliary variables and second-order cone constraint formulas are as follows:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, R ij , t, T ij,t , u i,t are all auxiliary variables, is the virtual voltage. If α η = 1, then there is If α η = 0, then there is

[0109] As a preferred embodiment, in the second aspect of the present invention, the fourth modeling unit includes:

[0110] The optimization subunit is used to aim at the optimal operation economy of the distribution network, and selects the objective function as the lowest sum of the maintenance cost and the system power loss of the distribution network system, specifically as follows:

[0111]

[0112] The coupling constraint subunit is used to add the coupling constraints of distribution network maintenance and reconfiguration on the basis of the constraints of the second modeling unit and the third modeling unit as follows:

[0113]

[0114]

[0115]

[0116] In the formula, R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of node i and node j respectively. If α η = 1, then there is If α η = 0, then there is The same applies to node j;

[0117] A sub-unit is added to add the electric vehicle charging pile load to the original IEEE33-node system based on the electric vehicle charging load model established by the first modeling unit, and the effectiveness of the model is verified through simulation analysis in this system.

[0118] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0119] The present invention can fully consider the factors of electric vehicle charging load and line maintenance, and provides a powerful analysis tool for the optimal economic operation of the distribution network containing electric vehicle charging load. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0121] Figure 1 is a schematic flow chart of the collaborative optimization method for electric vehicle demand response and distribution network operation of the present invention;

[0122] Figure 2 is a schematic diagram of the normalized charging power curve in Scenario 1 of the present invention;

[0123] Figure 3 is a schematic diagram of the normalized charging power curve in Scenario 2 of the present invention;

[0124] Figure 4 is a schematic diagram of the comparison of the calculated line loss curves in Scenario 1 of the present invention;

[0125] Figure 5 is a schematic diagram of the distribution network topology before the maintenance period calculated in Scenario 1 of the present invention;

[0126] Figure 6 is a schematic diagram of the distribution network topology during the maintenance period calculated in Scenario 1 of the present invention;

[0127] Figure 7 It is a schematic diagram of the distribution network topology after the maintenance period calculated in Scenario 1 of the present invention;

[0128] Figure 8 It is a schematic diagram for comparing the line loss curves calculated in Scenario 1 of the present invention;

[0129] Figure 9 It is a schematic diagram of the distribution network topology before the maintenance period calculated in Scenario 2 of the present invention;

[0130] Figure 10 It is a schematic diagram of the distribution network topology during the maintenance period calculated in Scenario 2 of the present invention;

[0131] Figure 11 It is a schematic diagram of the distribution network topology after the maintenance period calculated in Scenario 2 of the present invention;

[0132] Figure 12 It is a structural block diagram of the collaborative optimization device for electric vehicle demand response and distribution network operation of the present invention. Detailed implementation manners

[0133] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0134] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects, rather than to describe a specific order. The terms "include" and "have" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusion. Exemplarily, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0135] Embodiment 1

[0136] Please refer to Figure 1 As shown, a collaborative optimization method for electric vehicle demand response and distribution network operation includes the following steps:

[0137] Step 1: Based on the electric vehicle user data in the target area, model the charging load of electric vehicles and model them separately for two scenario modes according to the user charging habits; the two scenario modes are "charging after the last driving" and "charging after each driving".

[0138] Specifically, it includes:

[0139] (1) Analyze the charging energy demand of electric vehicles according to the electric vehicle user data in the target area. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution.

[0140] (2) The analysis results show that the shapes of the daily charging power curves of large-scale electric vehicles are basically the same, while the charging power values at each moment have a certain randomness. Therefore, the research on the charging power demand of electric vehicles is decomposed into the research on the random characteristics of the charging power peak and the research on the shape of the charging power curve. The charging power peak is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape.

[0141] (3) Based on the charging behavior habits of users, it is divided into two charging scenarios, namely "charging after the last driving" and "charging after each driving". The former is defined as Scenario 1, and the latter is defined as Scenario 2. In these two charging scenarios, the normalized charging power curve is fitted with a Gaussian function respectively, and the characteristic value data of the charging power peak is recorded. The Gaussian function formula for fitting the normalized charging power curve is as follows:

[0142]

[0143]

[0144] In the formula, P1 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after the last driving" scenario mode; P2 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after each driving" scenario mode; the values of each coefficient in the formula are: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1. The schematic diagram of the normalized charging power curve of Scenario 1 is as Figure 2 shown; the schematic diagram of the normalized charging power curve of Scenario 2 is as Figure 3 shown.

[0145] Step 2: Based on the maintenance and operation characteristics of the distribution network and considering the maintenance cost, establish a mathematical model for the optimization of the distribution network maintenance and operation.

[0146] The specific implementation steps are as follows:

[0147] (1) Considering the economic operation of the distribution network, the objective function of the model is set to minimize the system maintenance cost, and its specific functional formula is as follows:

[0148]

[0149] In the formula, N is the number of nodes in the distribution network, L is the set of distribution network lines, and T is the total number of time periods; here each time period is equally taken as 1 h, so T is 24, H l,t is the maintenance cost of line l in time period t, and Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1.

[0150] (2) The model maintenance constraints mainly include the line maintenance status variable constraint and the line maintenance time variable constraint, and the specific constraint formulas are as follows:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159] In the formula, MD L,l is the duration that line l needs to be maintained, NT is the total number of hours of the maintenance plan, and are the start time and end time of the maintenance respectively; MT l on and MT l off are the shortest duration and minimum time interval of the maintenance section of line l respectively; p l,t and q l,t are the start index and end index of the maintenance respectively; p l,t = 1 indicates the start of the maintenance, otherwise p l,t = 0; q l,t = 1 indicates the end of the maintenance, otherwise ql,t = 0; τ is the maintenance time.

[0160] Step 3: According to the basic strategy of distribution network reconfiguration and the requirements of economy and security in the power system, establish a mathematical model for the operation optimization of distribution network reconfiguration. The specific implementation steps are as follows:

[0161] (1) Considering minimizing the network loss of the distribution network system, the objective function of the model is selected as follows:

[0162]

[0163] In the formula, N is the number of nodes in the distribution network, i is the node number, is the injection power of node i;

[0164] (2) The relevant constraints of the distribution network reconfiguration model mainly include node power balance, node voltage amplitude constraint, line current amplitude constraint, and radial topology constraint of the distribution network, which are specifically as follows:

[0165]

[0166]

[0167]

[0168]

[0169] β ij,t +β ji,t =α ij,t

[0170]

[0171]

[0172] 0 ≤ α ij,t ≤ 1

[0173] In the formula, is the active load of node i at time period t, is the electric vehicle charging load of node i at time period t; is the injected reactive power of node i at time period t, is the reactive load of node i at time period t; θ ij is the voltage phase difference, g ij and b ij are the conductance and susceptance of the line ij (the line between node i and node j) in the π-type equivalent circuit respectively; V i,t and V j,t are the voltage amplitudes of node i and node j at time period t respectively; V i,max and Vi,min are the upper and lower limits of the voltage amplitude of node i; I ij,t is the current in line ij during time period t, I ijmax is the maximum allowable current in line ij, b sij is the shunt susceptance of line ij in the π-equivalent circuit, U i,t and U j,t are the voltages of node i and node j during time period t respectively; β ij,t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij,t is the line connection status variable, also a binary variable, which is equal to 1 indicating that line ij is in the connected state at time period t and 0 indicating that line ij is disconnected at time period t, A ij 、B ij 、C ij 、D ij are all simplified parameters.

[0174] (3) By using the second-order cone programming method, the distribution network reconfiguration problem is transformed from a difficult-to-solve mixed-integer non-linear programming problem into an easy-to-solve mixed second-order cone programming problem. The relevant auxiliary variables and second-order cone constraint formulas are as follows:

[0175]

[0176]

[0177]

[0178]

[0179] In the formula, R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of node i and node j respectively. If α η = 1, then there is If α η = 0, then there is The same applies to node j.

[0180] Step 4: Taking the minimum of the system power loss and the system maintenance cost as the objective, introducing the coupling constraints of maintenance and reconfiguration and the factor of the access of electric vehicle charging load, an operation optimization model of maintenance collaborative distribution network reconfiguration considering the access of electric vehicles is established. The specific implementation steps are as follows:

[0181] (1) With the goal of optimizing the operation economy of the distribution network, the objective function is selected as the minimum sum of the maintenance cost and the network loss of the distribution network system, as follows:

[0182]

[0183] (2) Based on the constraints in steps 2 and 3, the coupling constraints of distribution network maintenance and reconstruction are added as follows:

[0184]

[0185]

[0186]

[0187] In the formula, Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1. p l,t and q l,t are the start index and end index of the maintenance respectively. p l,t =1 indicates the start of maintenance, otherwise p l,t =0. q l,t =0 indicates the end of maintenance, otherwise q l,t =0. α l,t and α l,t-1 are line connection status variables, both of which are binary variables. When they are equal to 1, it means that line l is in the connected state in time period t or t-1, and when they are 0, it means that line l is disconnected in time period t or t-1;

[0188] (3) Based on the electric vehicle charging load model established in step 1, electric vehicle charging piles are added to the original IEEE33-node system, and simulation analysis is carried out in this system to verify the effectiveness of the model. The comparison diagram of the calculated network loss curves in Scenario 1 is as shown in Figure 4 , and the calculated distribution network topology diagrams before, during, and after the maintenance period are as shown in Figure 5 , 6 , 7. The comparison diagram of the calculated network loss curves in Scenario 2 is as shown in Figure 8 , and the calculated distribution network topology diagrams before, during, and after the maintenance period are as shown in Figure 9 , 10 , 11.

[0189] Embodiment 2

[0190] Please refer to Figure 12 shown, a collaborative optimization device for electric vehicle demand response and distribution network operation, including:

[0191] The first modeling unit 21 is configured to model the electric vehicle charging load based on the electric vehicle user data of the target area, and perform modeling separately for two scenario modes classified according to the user charging habits; the two scenario modes are "charging after the last drive" and "charging after each drive" respectively;

[0192] The second modeling unit 22 is configured to establish a mathematical model for the optimal operation of the distribution network maintenance according to the maintenance operation characteristics of the distribution network and on the basis of considering the maintenance cost;

[0193] The third modeling unit 23 is configured to establish a mathematical model for the optimal operation of the distribution network reconfiguration according to the basic strategy of the distribution network reconfiguration and the requirements of economy and security in the power system;

[0194] The fourth modeling unit 24 is configured to establish an optimal operation model for the maintenance collaborative distribution network reconfiguration considering the access of electric vehicles with the goal of minimizing the system network loss and the system maintenance cost, introducing the coupling constraints of maintenance and reconfiguration and the factors of the access of the electric vehicle charging load.

[0195] Preferably, the first modeling unit 21 includes:

[0196] The demand analysis subunit is configured to analyze the electric vehicle charging energy demand according to the electric vehicle user data of the target area. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution;

[0197] The charging power research subunit is configured to decompose the research on the charging power demand of the electric vehicle into the research on the peak random characteristics of the charging power and the research on the shape of the charging power curve; the peak of the charging power is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape;

[0198] The fitting subunit is configured to respectively fit the normalized charging power curve with a Gaussian function in the two scenario modes and record the peak characteristic value data of the charging power; the Gaussian function for fitting the normalized charging power curve is:

[0199]

[0200]

[0201] In the formula, P1 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after the last drive" scenario mode; P2 *(t) is the Gaussian function that fits the normalized charging power curve in the "charging after each driving" scenario mode; the values of the coefficients in the formula are as follows: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1.

[0202] Preferably, the second modeling unit 22 includes:

[0203] The economic constraint subunit is used to consider the economic operation of the distribution network and set the model objective function to the lowest system maintenance cost. The specific functional formula is as follows:

[0204]

[0205] In the formula, N is the number of distribution network nodes, L is the set of distribution network lines, and T is the total number of time periods; H l,t is the maintenance cost of line l in time period t, and Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1;

[0206] The model maintenance constraint subunit is used to perform constraints according to the line maintenance status variable constraint and the line maintenance time variable constraint included in the model maintenance constraint. The specific constraint formula is as follows:

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] In the formula, MD L,l is the duration that line l needs to be maintained, NT is the total number of hours of the maintenance plan, and are the start time and end time of the maintenance respectively; MT l on and MT loff They are the shortest duration and the minimum time interval for the overhaul section of line l respectively; p l,t and q l,t They are the start index and the end index of the overhaul respectively; p l,t = 1 indicates the start of the overhaul, otherwise p l,t = 0; q l,t = 1 indicates the end of the overhaul, otherwise q l,t = 0; τ is the overhaul time.

[0216] Preferably, the third modeling unit 23 includes:

[0217] A network loss constraint sub-unit, which is used to consider minimizing the network loss of the distribution network system, and the selected model objective function is as follows:

[0218]

[0219] In the formula, N is the number of nodes in the distribution network, i is the node number, is the injection power of node i;

[0220] A distribution network reconstruction model constraint sub-unit, which is used to perform constraints according to the relevant constraints of the distribution network reconstruction model, including node power balance, node voltage amplitude constraint, line current amplitude constraint, and distribution network radial topology constraint, specifically as follows:

[0221]

[0222]

[0223]

[0224]

[0225] β ij,t +β ji,t = α ij,t

[0226]

[0227]

[0228] 0 ≤ α ij,t ≤ 1

[0229] In the formula, is the active load of node i in time period t, is the electric vehicle charging load of node i in time period t; is the injected reactive power of node i in time period t, is the reactive load of node i in time period t; θ ijis the voltage phase difference, g ij and b ij are the conductance and susceptance of line ij in the π - type equivalent circuit respectively; V i,t and V j,t are the voltage amplitudes of node i and node j at time period t respectively; V i,max and V i,min are the upper and lower limits of the voltage amplitude of node i respectively; I ij,t is the current in line ij at time period t, I ijmax is the maximum allowable current in line ij, b sij is the shunt susceptance of line ij to ground in the π - type equivalent circuit, U i,t and U j,t are the voltages of node i and node j at time period t respectively; β ij,t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij,t is the line connection status variable, also a binary variable, which is equal to 1 indicating that line ij is in the connected state at time period t and equal to 0 indicating that line ij is disconnected at time period t, A ij 、B ij 、C ij 、D ij are all simplified parameters;

[0230] The conversion sub - unit is used to transform the distribution network reconfiguration problem from a difficult - to - solve mixed - integer non - linear programming problem into an easy - to - solve mixed second - order cone programming problem by the second - order cone programming method. Its related auxiliary variables and second - order cone constraint formulas are as follows:

[0231]

[0232]

[0233]

[0234]

[0235] In the formula, R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of node i and node j respectively. If α η = 1, then there is If α η = 0, then there is The same applies to node j.

[0236] Preferably, the fourth modeling unit 24 includes:

[0237] An optimization subunit, which is used to select the objective function as the minimum sum of the maintenance cost and the system line loss of the distribution network system with the goal of the optimal operation economy of the distribution network, specifically as follows:

[0238]

[0239] A coupling constraint subunit, which is used to add the coupling constraints of distribution network maintenance and reconstruction on the basis of the constraints of the second modeling unit and the third modeling unit as follows:

[0240]

[0241]

[0242]

[0243] In the formula, α l,t and α l,t-1 are line connection state variables, and they are both binary variables. When they are equal to 1, it means that the line l is in the connected state during the time period t or t - 1, and when they are 0, it means that the line l is disconnected during the time period t or t - 1;

[0244] An addition subunit, which is used to add the electric vehicle charging pile load to the original IEEE33 - node system based on the electric vehicle charging load model established by the first modeling unit, and conduct simulation analysis in this system to verify the effectiveness of the model

[0245] The above has introduced in detail the collaborative optimization method and device for electric vehicle demand response and distribution network operation disclosed in the embodiments of the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A collaborative optimization method for electric vehicle demand response and distribution network operation, characterized in that Including: Step 1: Based on the electric vehicle user data in the target area, model the electric vehicle charging load and separately model it for two scenario modes based on user charging habits; the two scenario modes are "charging after the last trip" and "charging after each trip"; Step 2: Based on the maintenance and operation characteristics of the distribution network and considering the maintenance cost, establish a mathematical model for the optimization of the distribution network maintenance and operation; Step 3: Based on the basic strategy of distribution network reconfiguration and the requirements of economy and security in the power system, establish a mathematical model for the optimization of the distribution network reconfiguration operation; Step 4: With the goal of minimizing the system network loss and the system maintenance cost, introduce the coupling constraints of maintenance and reconfiguration and the factor of the access of electric vehicle charging load, and establish an operation optimization model for the coordinated distribution network reconfiguration considering the access of electric vehicles; The said Step 2 includes: Considering the economic operation of the distribution network, set the model objective function to minimize the system maintenance cost, and its specific functional formula is as follows: Wherein, L is the set of distribution network lines, and T is the total number of time periods; H l,t is the maintenance cost of line l in time period t, and Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1; The model maintenance constraints include the line maintenance status variable constraint and the line maintenance time variable constraint, and the specific constraint formulas are as follows: wherein, MD L,l is the duration for which the line l needs to be repaired, NT is the total number of hours of the repair plan, and are the start time and end time of the repair respectively; MT l on and MT l off are respectively the shortest duration and the minimum time interval of the repair section of the line l; p l,t and q l,t are respectively the start index and end index of the repair; p l,t = 1 indicates the start of the repair, otherwise p l,t = 0; q l,t = 1 indicates the end of the repair, otherwise q l,t = 0; τ is the repair time; The said Step 3 includes: Considering minimizing the distribution network system network loss, select the following model objective function: where N is the number of nodes in the distribution network, i is the node number, is the injection power of node i; The relevant constraints of the distribution network reconfiguration model include node power balance, node voltage amplitude constraint, line current amplitude constraint, and distribution network radial topology constraint, as follows: D ij = g ij b sij / 2 β ij,t +β ji,t ＝α ij,t 0≤α ij,t ≤1 wherein, is the active power load of node i at time period t, is the electric vehicle charging load of node i at time period t; is the injected reactive power of node i at time period t, is the reactive power load of node i at time period t; θ ij is the voltage phase difference, g ij and b ij are the conductance and susceptance of line ij in the π-type equivalent circuit respectively; V i,t and V j,t are the voltage amplitudes of node i and node j at time period t respectively; V i,max and V i,min are the upper and lower limits of the voltage amplitude of node i respectively; I ij,t is the current in line ij at time period t, I ijmax is the maximum allowable current in line ij, b sij is the shunt susceptance of line ij to the ground in the π-type equivalent circuit, U i,t and U j,t are the voltages of node i and node j at time period t respectively; β ij,t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij,t is the line connection status variable, also a binary variable, which is equal to 1 indicating that line ij is in the connected state at time period t and is 0 indicating that line ij is disconnected at time period t, A ij , B ij , C ij , D ij are all simplified parameters; By using the second-order cone programming method, transform the distribution network reconfiguration problem from a difficult-to-solve mixed integer non-linear programming problem into an easy-to-solve mixed second-order cone programming problem, and its relevant auxiliary variables and second-order cone constraint formulas are as follows: wherein, R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of node i and node j respectively. If α η = 1, then If α η = 0, then The same applies to node j.

2. The collaborative optimization method for electric vehicle demand response and distribution network operation according to claim 1, wherein The said Step 1 includes: Based on the electric vehicle user data in the target area, analyze the electric vehicle charging energy demand. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution; Decompose the research on the charging power demand of electric vehicles into the research on the peak random characteristics of the charging power and the research on the shape of the charging power curve; the peak of the charging power is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape; In the two scenario modes, respectively fit the normalized charging power curve with a Gaussian function and record the peak characteristic value data of the charging power; the Gaussian function for fitting the normalized charging power curve is: wherein, P1 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after the last driving" scenario mode; P2 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after each driving" scenario mode; the values of the coefficients in the formula are respectively: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1.

3. The collaborative optimization method for electric vehicle demand response and distribution network operation according to claim 1, characterized in that The said Step 4 includes: With the goal of the optimal economy of the distribution network operation, select the objective function to be the minimum of the sum of the distribution network system maintenance cost and the system network loss, as follows: On the basis of the constraints in Steps 2 and 3, add the coupling constraints of the distribution network maintenance and reconfiguration as follows: where α l,t and α l,t-1 are line connection status variables and are also binary variables. When they are equal to 1, it means that line l is in the connected state during time period t or t - 1, and when they are equal to 0, it means that line l is disconnected during time period t or t - 1; Based on the electric vehicle charging load model established in Step 1, add the electric vehicle charging pile load to the original IEEE33-node system, and conduct simulation analysis in this system to verify the effectiveness of the model.

4. A collaborative optimization device for electric vehicle demand response and distribution network operation, characterized in that, Including: The first modeling unit is used to model the electric vehicle charging load based on the electric vehicle user data in the target area, and separately model it for two scenario modes based on user charging habits; the two scenario modes are "charging after the last trip" and "charging after each trip"; The second modeling unit is used to establish a mathematical model for optimizing the maintenance and operation of the distribution network based on the maintenance and operation characteristics of the distribution network and considering the maintenance cost; The third modeling unit is used to establish a mathematical model for optimizing the reconstruction operation of the distribution network according to the basic strategy of distribution network reconstruction and the requirements of economy and security in the power system; The fourth modeling unit is used to establish a coordinated maintenance distribution network reconstruction operation optimization model considering the access of electric vehicles with the goal of minimizing the system network loss and the system maintenance cost, introducing the coupling constraints of maintenance and reconstruction and the factor of the access of electric vehicle charging load; The second modeling unit includes: The economic constraint sub-unit is used to consider the economic operation of the distribution network, set the model objective function as the minimum system maintenance cost, and its specific functional formula is as follows: Wherein, L is the set of distribution network lines, and T is the total number of time periods; H l,t is the maintenance cost of line l in time period t, and Y l,t is the line maintenance status of line l in time period t. When line l is in the maintenance state, Y l,t is 0, otherwise Y l,t is 1; The model maintenance constraint sub-unit is used to perform constraints according to the line maintenance state variable constraint and the line maintenance time variable constraint included in the model maintenance constraint, and the specific constraint formula is as follows: wherein, MD L,l is the duration for which the line l needs to be repaired, NT is the total number of hours of the repair plan, and are the start time and end time of the repair respectively; MT l on and MT l off are respectively the shortest duration of the repair segment and the minimum time interval of the line l; p l,t and q l,t are respectively the start index and end index of the repair; p l,t = 1 indicates the start of the repair, otherwise p l,t = 0; q l,t = 1 indicates the end of the repair, otherwise q l,t = 0; τ is the repair time; The third modeling unit includes: The network loss constraint sub-unit is used to consider minimizing the system network loss of the distribution network, and select the model objective function as follows: where N is the number of nodes in the distribution network, i is the node number, is the injection power of node i; The distribution network reconstruction model constraint sub-unit is used to perform constraints according to the relevant constraints of the distribution network reconstruction model, including node power balance, node voltage amplitude constraint, line current amplitude constraint, and distribution network radial topology constraint, as follows: β ij,t +β ji,t ＝α ij,t 0≤α ij,t ≤1 Wherein, is the active load of node i at time period t, is the electric vehicle charging load of node i at time period t; is the reactive power injection of node i at time period t, is the reactive load of node i at time period t; θ ij is the voltage phase difference, g ij and b ij are the conductance and susceptance of line ij in the π-type equivalent circuit respectively; V i,t and V j,t are the voltage amplitudes of node i and node j at time period t respectively; V i,max and V i,min are the upper and lower limits of the voltage amplitude of node i respectively; I ij,t is the current in line ij at time period t, I ijmax is the maximum allowable current in line ij, b sij is the shunt susceptance of line ij to ground in the π-type equivalent circuit, U i,t and U j,t are the voltages of node i and node j at time period t respectively; β ij,t is a binary variable indicating that node i is the parent node of node j at time period t; R(i) is the set of nodes connected to node i; S(i) is the set of substation nodes; α ij,t is the line connection status variable, which is also a binary variable. When it is equal to 1, it means that line ij is in the connected state at time period t, and when it is 0, it means that line ij is disconnected at time period t, A ij 、B ij 、C ij 、D ij are all simplified parameters; The conversion sub-unit is used to convert the distribution network reconstruction problem from a difficult-to-solve mixed integer non-linear programming problem into an easy-to-solve mixed second-order cone programming problem by the second-order cone programming method, and its relevant auxiliary variables and second-order cone constraint formulas are as follows: where R ij,t , T ij,t , u i,t are all auxiliary variables, and are the virtual voltages of nodes i and j respectively. When α η = 1, then there is When α η = 0, then there is The same applies to node j.

5. The collaborative optimization device for electric vehicle demand response and distribution network operation according to claim 4, wherein The first modeling unit includes: The demand analysis sub-unit is used to analyze the electric vehicle charging energy demand according to the electric vehicle user data in the target area. The total charging energy demand of a large number of electric vehicles can be approximated by a normal distribution, and the charging energy demand of a single vehicle can be approximated by a Weibull distribution; The charging power research sub-unit is used to decompose the research on the charging power demand of electric vehicles into the research on the peak random characteristics of the charging power and the research on the shape of the charging power curve; the peak value of the charging power is described by a probability distribution model, and the shape of the charging power curve is described by the normalized charging curve shape; The fitting sub-unit is used to fit the normalized charging power curve with a Gaussian function in the two scenario modes respectively and record the peak characteristic value data of the charging power; the Gaussian function for fitting the normalized charging power curve is: Wherein, P1 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after the last driving" scenario mode; P2 * (t) is the Gaussian function for fitting the normalized charging power curve in the "charging after each driving" scenario mode; the values of the coefficients in the formula are respectively: α = 0.5, β = 3.75, m = 19.5, n = 2.25, α1 = 0.5, β1 = 2.5, n1 = 9, m1 = 1, β2 = 2, n2 = 19.5, m2 = 1.

6. The collaborative optimization device for electric vehicle demand response and distribution network operation according to claim 4, characterized in that, The fourth modeling unit includes: The optimization sub-unit is used to take the optimal economy of the distribution network operation as the goal, and select the objective function as the minimum sum of the system maintenance cost and the system network loss of the distribution network, as follows: The coupling constraint sub-unit is used to add the coupling constraints of distribution network maintenance and reconstruction on the basis of the constraints of the second modeling unit and the third modeling unit as follows: where α l,t and α l,t-1 are line connection status variables and are also binary variables. When they are equal to 1, it means that line l is in a connected state during time period t or t - 1, and when they are equal to 0, it means that line l is disconnected during time period t or t - 1; The addition sub-unit is used to add the electric vehicle charging pile load to the original IEEE33 node system based on the electric vehicle charging load model established by the first modeling unit, and perform simulation analysis in this system to verify the effectiveness of the model.

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