Reactive power optimization method for distribution network considering reactive power support provided by electric vehicle chargers

Abstract

The present application relates to the field of power system management and optimization, and discloses a distribution network reactive power optimization method considering reactive power support provided by electric vehicle chargers, including the following steps: S1, establishing an electric vehicle operation model considering reactive power support provided by electric vehicle chargers; S2, establishing an on-load tap-changing transformer and capacitor bank operation model; S3, constructing distribution network flow constraints based on a second-order cone relaxation algorithm; S4, based on the variables and constraints established above, taking the lowest comprehensive operation cost of the distribution network as the objective function, constructing a distribution network reactive power optimization model and its solution method considering reactive power support provided by electric vehicle chargers. The present invention can effectively smooth the load curve of the power grid and reduce the load pressure during peak hours through orderly control of electric vehicle charging and discharging. By considering reactive power support of charging piles, the number of actions and switching gears of traditional reactive power / voltage regulating equipment in the distribution network can be effectively reduced, the system network loss can be reduced, and the economy of distribution network operation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management and optimization, and in particular to a distribution network reactive power optimization method taking into account reactive power support provided by electric vehicle chargers. Background Art

[0002] With the rapid growth of electric vehicles, their impact on the power grid is becoming increasingly significant, especially at the distribution network level. As mobile power loads and energy storage resources, the charging behavior of electric vehicles directly affects the operational safety and economy of the distribution network. Since the charging demand of electric vehicles is highly related to the load fluctuations of the power grid and the uncertainty of energy supply, how to effectively manage the charging and discharging of electric vehicles and optimize their load scheduling is a major challenge in current power grid management.

[0003] The charging demand of electric vehicle clusters is usually concentrated during peak hours. If not managed, it may cause overload of the power grid and affect the stability of the power grid. Traditional power grid dispatching methods often fail to fully consider the potential reactive power support capacity of electric vehicle charging piles, and fail to fully utilize the advantages of electric vehicles as distributed energy storage, resulting in insufficient allocation of power grid resources and inability to effectively cope with peak power demand.

[0004] In addition, with the widespread access to distributed energy resources such as wind power and solar energy, the complexity of grid operation has further increased. The charging load characteristics of electric vehicles and their coupled scheduling with renewable energy have a particularly significant impact on the grid. Therefore, a new grid management method is needed that can comprehensively consider the charging characteristics of electric vehicles, the volatility of distributed energy resources, and the operation requirements of the grid to optimize grid operation and improve the reliability and economy of the grid. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a distribution network reactive power optimization method that takes into account the reactive power support provided by electric vehicle chargers. By effectively utilizing the characteristics of electric vehicles as mobile power sources and energy storage units, the active and reactive power scheduling of the power grid is optimized. By achieving efficient coordination between electric vehicle clusters and distribution networks, the economy and stability of the power grid are improved. At the same time, the peak load on the power grid caused by electric vehicle charging is reduced, the adaptability of the power grid to distributed energy fluctuations is enhanced, and the voltage quality and power supply reliability of the power grid are improved through the reactive power support of electric vehicles.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a distribution network reactive power optimization method considering that an electric vehicle charger provides reactive power support, comprising the following steps:

[0007] Establish an electric vehicle operation model that considers the reactive power support provided by the electric vehicle charger, including its variables and constraints;

[0008] Establish the operation model of the on-load tap-changing transformer and capacitor bank, including its variables and constraints;

[0009] Based on the second-order cone relaxation algorithm, the power flow constraints of the distribution network are constructed, including active power and reactive power balance constraints and node voltage and branch current safety constraints;

[0010] Based on the variables and constraints established above, taking the comprehensive operation cost of the distribution network as the objective function, a distribution network reactive power optimization model and its solution method considering the reactive power support provided by electric vehicle chargers are constructed.

[0011] Preferably, the electric vehicle operation model includes:

[0012] Define variables: Electric vehicle charging and discharging power variables and Charge and discharge state variables and Battery charge variable E m,t , charger active and reactive power variables and

[0013] Based on the above variables and electric vehicle parameters, the electric vehicle operation model is constructed as follows:

[0014]

[0015] in, are the charging power, discharging power and rated charging and discharging power of electric vehicle m at time t, and are the charge and discharge states of electric vehicle m at time t, Equal to 1 indicates charging state, Equal to 1 indicates the discharge state, and The value of cannot be 1 at the same time. It is the active power flowing in both directions of the electric vehicle charger. Positive indicates charging, and negative indicates discharging. It is the reactive power flowing in both directions of the electric vehicle charger. A positive value indicates reactive power is released, and a negative value indicates reactive power is absorbed. is the apparent power of the electric vehicle charger, E m,t They represent the expected charge capacity, initial charge capacity and charge capacity of vehicle m at time t respectively; and They represent the upper and lower limits of the energy boundary during the charging and discharging process of vehicle m; η ch , η dis is the charging and discharging efficiency of the vehicle, and Δt is the time interval between two scheduling times.

[0016] Preferably, the on-load tap-changing transformer and capacitor bank operation model includes:

[0017] Define the variables of the on-load transformer operation model: the action state variable o of the on-load tap changer k at time t k,t , the scalar value of the gear up and down of the load-adjusting transformer at time t and

[0018] Based on the above variables and on-load transformer parameters, the on-load transformer operation model is constructed as follows:

[0019]

[0020] Among them, U 0,t is the square value of the voltage at the root node of the high-voltage side of the on-load tap-changing transformer at time t; a is the ratio difference of the adjacent gears of the on-load tap-changing transformer; o k,t It represents the operating state of the tap changer k of the on-load tap-changing transformer at time t and is a 0-1 variable. 0-1 marks respectively representing the gear up and down of the on-load tap-changing transformer at time t; Indicates the maximum range of change of the gear position of the on-load tap-changing transformer;

[0021] Limit the number of operations of the on-load transformer in one day. The specific constraints are as follows:

[0022]

[0023] Among them, n oltc,max Indicates the maximum number of operations allowed for the on-load tap-changing transformer in one day;

[0024] Define the variables of the capacitor bank operation model: c n,t It represents the identification variable of the nth group of capacitors put into operation at time t. are the identifier variables indicating the increase and decrease of the number of capacitor banks put into operation at time t;

[0025] Based on the above variables and on-load transformer parameters, the capacitor bank operation model is constructed as follows:

[0026]

[0027] Among them, q cap Indicates the reactive power provided when a single group of capacitors is put into operation, c n,t The 0-1 identification variable represents the number of capacitors in the nth group at time t. 0-1 variables indicating the increase or decrease of the number of capacitor banks at time t, Indicates the maximum range of variation of the number of capacitor groups put into operation;

[0028] Limit the number of operations of the capacitor bank in one day. The specific constraints are as follows:

[0029]

[0030] Among them, n CB,max Indicates the maximum number of operations allowed for the capacitor bank in one day.

[0031] Preferably, the construction of distribution network power flow constraints based on the second-order cone relaxation algorithm includes:

[0032]

[0033] Among them, β j represents the set of terminal nodes of the branch with node j as the first node, α j represents the set of the first-end nodes of the branch with node j as the terminal node, P ij,t and Q ij,t They represent the active power and reactive power transmitted by the branch at time t, respectively, ij and r ij They represent the line resistance and reactance of branch ij at time t, and They represent the active power and reactive power consumed by the conventional load of node j at time t, and They represent the charging power, discharging power and reactive power of EVA at node j at time t, and They represent the active power and reactive power injected from the root node at time t, is the active power injected by the distributed generation at node j at time t, is the reactive power injected by the capacitor bank at node j at time t, I ij,t is the square value of the current flowing through branch ij at time t, that is, U i,t is the square value of the voltage at node i at time t, that is

[0034] Safety constraints on node voltage and branch current:

[0035]

[0036] Among them, u i,min 、u i,max are the upper and lower limits of the voltage amplitude allowed for node i, ij,max is the upper limit of the transmission current allowed in branch ij.

[0037] Preferably, the distribution network reactive power optimization model considering the reactive power support provided by the electric vehicle charger and its solution method include:

[0038] The comprehensive operating cost of the active distribution network is taken as the objective function. The comprehensive operating cost mainly includes the distribution network power purchase cost, the penalty cost of wind and solar power abandonment, network loss cost, and reactive power / voltage control equipment adjustment cost:

[0039] minF1=f buy +f DG +f loss +f QV

[0040] Among them, f buy represents the system electricity purchase cost, f DG represents the penalty cost of abandoning wind and solar power, f loss represents the network loss cost, f QV represents the regulation cost of traditional reactive / voltage control equipment;

[0041] The system power purchase cost expression is as follows:

[0042]

[0043] in, P represents the price of electricity purchased by the distribution network from the upstream power grid in the t period, grid,t It represents the active power transmitted from the upstream power grid to the distribution network in the tth period, T is the day-ahead dispatching time length, and Δt is the time interval;

[0044] The penalty cost expression for wind and solar power abandonment is as follows:

[0045]

[0046] Among them, N DG represents the node set of distributed generation connected to the distribution network, c pen represents the unit power curtailment cost, represents the actual output of the distributed generation at node j during period t, represents the predicted output of the distributed generation at node j in period t;

[0047] The network loss cost expression is as follows:

[0048]

[0049] Where L represents the set of transmission lines in the distribution network, c loss represents the unit power loss cost, represents the active power loss of branch ij in period t;

[0050] The expression of reactive power / voltage control equipment regulation cost is as follows:

[0051]

[0052] Among them, N CB is the node set of the capacitor bank connected to the distribution network, c oltc 、c CB Respectively represent the cost of one-time operation of on-load tap-changing transformer and capacitor bank, n oltc 、n CB,j Respectively represent the number of operations of the load-regulating transformer and capacitor bank in one day;

[0053] By using the reactive power optimization model of the distribution network that considers reactive power support provided by electric vehicle chargers, based on the wind and solar output and conventional load forecast data in a future dispatching cycle, combined with the traditional reactive power / voltage regulation equipment of the distribution network, the charging and discharging power of electric vehicles, the gear position of the on-load tap-changing transformer, the switching gear position of the capacitor bank, and the day-ahead plan of the distribution network to purchase electricity from the upstream power grid are optimized;

[0054] The reactive power optimization model of the distribution network is regarded as a mixed integer second-order cone programming problem, and its expression can be simplified as follows:

[0055] min cx

[0056] stAx≤b

[0057] Bx=d

[0058] ||Hx||2≤hx

[0059] Among them, c, A, b, B, d, H, h constitute the correlation coefficient matrix of the objective function and the constraint conditions, Ax≤b and Bx=d constitute a series of inequality and equality constraints of the model respectively, and ||Hx||2≤hx represents the second-order cone inequality constraint in the model;

[0060] Solved using a mathematical optimization solver.

[0061] The present invention also provides a distribution network reactive power optimization device that considers reactive power support provided by an electric vehicle charger, including:

[0062] Data acquisition module, used to obtain distribution network operation data, electric vehicle charging data, and on-load transformer and capacitor bank parameter data;

[0063] The prediction module is used to predict the wind, solar and base load for the next day;

[0064] An optimization scheduling module is used to construct a distribution network reactive power optimization scheduling model based on the parameters and prediction data, and generate a scheduling plan;

[0065] The control and communication module is used to execute the scheduling plan generated by the optimization scheduling module and issue specific charging and discharging instructions and reactive support instructions to each electric vehicle.

[0066] The present invention also provides a computer device, 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, the above method is implemented.

[0067] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0068] The present invention provides a distribution network reactive power optimization method taking into account reactive power support provided by an electric vehicle charger.

[0069] It has the following beneficial effects:

[0070] 1. The present invention can effectively smooth the load curve of the power grid and reduce the load pressure during peak hours through orderly control of electric vehicle charging and reactive power support. By shifting the charging demand of electric vehicles from peak hours to trough hours and using electric vehicles to provide reactive power support for the power grid, the power supply pressure of the power grid during peak hours can be reduced, thereby reducing the dependence on the peak load shaving resources of the power grid and improving the economy and stability of the power grid operation.

[0071] 2. The reactive power support function of electric vehicles implemented in the present invention can provide necessary reactive power compensation at different network nodes, especially at the end nodes of the power grid. By intelligently regulating the charging and discharging behavior of electric vehicles, charging piles can provide reactive power according to real-time demand, which helps to maintain the stability of system voltage, avoid voltage quality problems caused by large-scale grid-connected charging of electric vehicles, and improve the overall safety and reliability of the power grid.

[0072] 3. Through the cluster management and dispatching of electric vehicles, the present invention can more efficiently utilize renewable energy sources, such as wind power and solar energy. The day-ahead and real-time dispatching models enable the power grid to dynamically adjust the charging plan of electric vehicles according to the predicted output and actual output of renewable energy, reduce the impact of fluctuations in the output of new energy, and optimize the energy structure and operating efficiency of the distribution network.

[0073] 4. By dynamically adjusting the charging and discharging strategies of electric vehicles and providing reactive power support, the present invention can reduce the operating costs of the power grid. The system can reduce the additional peak load caused by charging electric vehicles, alleviate the operating pressure of power grid equipment, and extend its service life. At the same time, by effectively managing the charging and discharging of electric vehicles, the demand for the power grid to purchase peak-price electricity can be reduced, thereby reducing economic costs.

[0074] 5. The present invention optimizes the charging time and method of electric vehicles, so that the distribution network can better integrate renewable energy, reduce dependence on fossil energy, and reduce carbon emissions. Through intelligent management and the use of electric vehicles as energy storage units, energy feedback can be provided during peak demand, which helps to improve the environmental friendliness and sustainability of the entire power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0076] Figure 2 It is a schematic diagram of the topology of the improved IEEE-33 node system in the present invention;

[0077] Figure 3 This is a schematic diagram of charging and discharging power of an electric vehicle cluster in scenario 1 of the present invention;

[0078] Figure 4 This is a schematic diagram of a voltage curve of an electric vehicle access node in scenario 1 of the present invention;

[0079] Figure 5 This is a schematic diagram of charging and discharging power of an electric vehicle cluster in scenario 2 of the present invention;

[0080] Figure 6 A schematic diagram of charging and discharging power and reactive compensation power of an electric vehicle cluster in scenario three of the present invention;

[0081] Figure 7 It is a schematic diagram of the operation of the on-load voltage regulating device in different scenarios of the present invention;

[0082] Figure 8 This is a schematic diagram of the operation of the capacitor bank in scenario 2 of the present invention;

[0083] Fig. 9 This is a schematic diagram of the operation of the capacitor bank in scenario three of the present invention;

[0084] Fig.10 Schematic diagram of voltage curve of electric vehicle access node in different scenarios of the present invention;

[0085] Fig.11 It is a schematic diagram of the structure of the device of the present invention;

[0086] Fig.12 It is a schematic diagram of the computer device structure of the present invention.

[0087] Among them, 100, data acquisition module; 200, prediction module; 300, optimization scheduling module; 400, control and communication module; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0088] The following will be combined with the drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Please refer to the attached Figure 1 The embodiment of the present invention provides a reactive power optimization method for a distribution network considering reactive power support provided by an electric vehicle charger, comprising the following steps:

[0090] S1. Establish an electric vehicle operation model that considers the reactive power support provided by the electric vehicle charger, including its variables and constraints.

[0091] In this embodiment, a detailed electric vehicle operation model needs to be established first to fully consider the ability of the electric vehicle charger to provide reactive support. By defining key variables and their constraints, we can accurately describe the charging and discharging behavior of electric vehicles and optimize their impact on the distribution network. The model mainly includes the charging and discharging power, state, SOC (state of charge) of electric vehicles, and the balance of active and reactive power.

[0092] Variables and constraints

[0093] In order to fully consider the charging and discharging process of electric vehicles, this model defines the following variables and constraints:

[0094] variable:

[0095] Charging power for electric vehicles and discharge power

[0096] Charge and discharge state variables and

[0097] Battery charge variable E m,t ;

[0098] Charger active power and reactive power

[0099] Constraints:

[0100] Upper and lower limits of charge and discharge power:

[0101]

[0102] The charge and discharge states cannot be 1 at the same time:

[0103]

[0104] Active power balance of electric vehicle charger:

[0105]

[0106] Apparent power limitation for active and reactive power:

[0107]

[0108] Dynamic update of battery level:

[0109]

[0110] The upper and lower limits of battery power:

[0111]

[0112] Initial battery capacity and expected battery capacity:

[0113]

[0114] in:

[0115] and are the charging power and discharging power of electric vehicle m at time t respectively;

[0116] is the rated charging and discharging power of the electric vehicle;

[0117] and is the charging and discharging state of the electric vehicle at time t, 1 indicates charging status, 1 indicates the discharge state, and both cannot be 1 at the same time;

[0118] It is the active power flowing in both directions of the electric vehicle charger, with positive values ​​indicating charging and negative values ​​indicating discharging;

[0119] It is the reactive power flowing in both directions of the electric vehicle charger. A positive value indicates reactive power is released, and a negative value indicates reactive power is absorbed.

[0120] is the apparent power of the electric vehicle charger;

[0121] and They represent the expected charge capacity and initial charge capacity of vehicle m respectively;

[0122] and They represent the upper and lower limits of energy during the charging and discharging process of vehicle m respectively;

[0123] η ch and η dis are the charging and discharging efficiency of the vehicle respectively;

[0124] Δt is the time interval between two scheduling times.

[0125] The electric vehicle operation model of this embodiment can effectively describe the charging and discharging behavior of the electric vehicle by defining and constraining the above variables, while taking into account the reactive support capacity of the electric vehicle charger to ensure the stable operation of the distribution network.

[0126] S2. Establish the operation model of the on-load tap-changing transformer and capacitor bank, including its variables and constraints

[0127] In this embodiment, an operation model of an on-load tap-changing transformer and a capacitor bank is established to effectively manage reactive power regulation in the distribution network. These two devices are important components for achieving reactive power optimization and can maintain stable operation of the distribution network by adjusting voltage and reactive power. The following describes in detail the operation model of the on-load tap-changing transformer and the capacitor bank, including variable definitions and constraints.

[0128] Operation model of on-load tap-changing transformer

[0129] First, define the variables of the on-load tap-changing transformer operation model:

[0130] The action state variable i of the tap changer k of the on-load tap-changing transformer at time t k,t .

[0131] The sign variable of the on-load tap-changing transformer when the gear is rising or falling at time t and

[0132] Based on the above variables and the parameters of the on-load tap-changing transformer, the operation model of the on-load tap-changing transformer is established as follows:

[0133]

[0134] in:

[0135] U 0,t It is the square value of the voltage at the root node on the high voltage side of the on-load tap-changing transformer at time t.

[0136] a is the transformation ratio difference between adjacent gears of the on-load tap-changing transformer.

[0137] o k,t It represents the operating state of the tap changer k of the on-load tap-changing transformer at time t and is a 0-1 variable.

[0138] and The 0-1 marks respectively indicate whether the gear of the on-load tap-changing transformer is up or down at time t. Indicates the maximum range of change of the gear position of the on-load tap-changing transformer.

[0139] Frequent operation of the on-load transformer will affect its service life, so the number of operations in one day should be limited. The specific constraints are as follows:

[0140]

[0141] Among them, n oltc,max Indicates the maximum number of operations allowed for the on-load tap-changing transformer in one day.

[0142] Capacitor Bank Operation Model

[0143] Define the variables for the capacitor bank operation model:

[0144] c n,t It represents the identification variable of the nth group of capacitors put into operation at time t.

[0145] and They represent the identification variables of the increase and decrease of the number of capacitor groups put into operation at time t.

[0146] Based on the above variables and capacitor bank parameters, the capacitor bank operation model is established as follows:

[0147]

[0148] in:

[0149] q cap Indicates the reactive power provided when a single group of capacitors is put into operation.

[0150] c n,t A 0-1 identification variable representing the nth group of capacitors put into operation at time t.

[0151] and 0-1 identification variables representing the increase or decrease in the number of capacitor banks put into operation at time t.

[0152] Indicates the maximum range of variation of the number of capacitor groups put into operation.

[0153] Similar to the on-load voltage regulator, it is necessary to limit the number of operations of the capacitor bank in one day to extend its service life. The specific constraints are as follows:

[0154]

[0155] Among them, nCB,max Indicates the maximum number of operations allowed for the capacitor bank in one day.

[0156] By establishing the operation model of on-load tap-changing transformers and capacitor banks, the voltage and reactive power in the distribution network can be effectively controlled and adjusted to ensure the stable and economical operation of the system. These models provide the necessary basis for subsequent reactive power optimization and dispatching.

[0157] S3. Construct distribution network power flow constraints based on the second-order cone relaxation algorithm, including active power and reactive power balance constraints and node voltage and branch current safety constraints

[0158] In this embodiment, a power flow constraint model of the distribution network is constructed based on the second-order cone relaxation algorithm. The model ensures the stable operation of the distribution network by considering the active power and reactive power balance constraints, node voltage constraints and branch current safety constraints. The specific content of the power flow constraint of the distribution network is described in detail below.

[0159] Distribution Network Flow Constraint Model Based on Second-Order Cone Relaxation Algorithm

[0160] Define variables:

[0161] P ij,t and Q ij,t They represent the transmission active power and reactive power of branch (i, j) at time t respectively.

[0162] I ij,t is the square value of the current flowing through branch (i, j) at time t, that is,

[0163] U i,t is the square value of the voltage at node i at time t, that is,

[0164] r ij and x ij They represent the resistance and reactance of branch (i, j) respectively.

[0165] and They represent the active power and reactive power of the conventional load at node j at time t respectively.

[0166] and They represent the EVA charging power and discharging power of node j at time t respectively.

[0167] Represents the EVA reactive power of node j at time t.

[0168] is the active power injected by the distributed generation at node j at time t.

[0169] is the reactive power injected by the capacitor bank at node j at time t.

[0170] and They represent the active power and reactive power injected from the root node at time t respectively.

[0171] Based on the above variables, the power flow constraint model of the distribution network is constructed as follows:

[0172] Active power and reactive power balance constraints

[0173]

[0174] Among them, β j represents the branch set with node j as the head node, α j Represents the set of branches with node j as the terminal node.

[0175] Node Voltage Constraints

[0176]

[0177] Second-order cone constraints

[0178]

[0179] Safety constraints on node voltage and branch current

[0180]

[0181]

[0182] Among them, u i,min 、u i,max are the upper and lower limits of the voltage amplitude allowed for node i, ij,max is the upper limit of the transmission current allowed for branch ij.

[0183] Through the above model, effective constraints on the power flow of the distribution network can be achieved, ensuring that the voltage, current and power of the system are within a safe and reasonable range at all times, thereby ensuring the stable operation of the distribution network.

[0184] S4. Based on the variables and constraints established above, taking the lowest comprehensive operation cost of the distribution network as the objective function, a distribution network reactive power optimization model and its solution method considering the reactive power support provided by electric vehicle chargers are constructed.

[0185] In this embodiment, based on the variables and constraints established above, a reactive power optimization model with the goal of minimizing the comprehensive operation cost of the distribution network is proposed, and the solution method of the model is described in detail. By optimizing the distribution network power purchase cost, the penalty cost of wind and solar power abandonment, the network loss cost and the reactive power / voltage control equipment adjustment cost, the reactive power support of the electric vehicle charger to the distribution network is realized, ensuring the economy and stability of the distribution network.

[0186] Optimizing the objective function

[0187] The objective function of this embodiment is the comprehensive operating cost of the active distribution network, which specifically includes the following four parts:

[0188] 1. System power purchase cost f buy

[0189]

[0190] in, P represents the price of electricity purchased by the distribution network from the upstream power grid in the t period, grid,t It represents the active power transmitted from the upstream power grid to the distribution network in the tth period, T is the day-ahead dispatching time length, and Δt is the time interval.

[0191] 2. Penalty costs for curtailing wind and solar power generation DG

[0192]

[0193]

[0194] Among them, N DG represents the node set of distributed generation connected to the distribution network, c pen represents the unit power curtailment cost, represents the actual output of the distributed generation at node j during period t, Represents the predicted output of the distributed generation at node j in period t.

[0195] 3. Network loss cost loss

[0196]

[0197] Where L represents the set of transmission lines in the distribution network, c loss represents the unit power loss cost, Represents the active power loss of branch ij during period t.

[0198] 4. Reactive power / voltage control equipment adjustment cost f QV

[0199]

[0200] Among them, N CB is the node set of the capacitor bank connected to the distribution network, c oltc 、c CB Respectively represent the cost of one-time operation of on-load tap-changing transformer and capacitor bank, n oltc 、n CB,j Respectively represent the number of operations of the load-regulating transformer and capacitor bank in one day.

[0201] In summary, the objective function of the comprehensive operating cost is:

[0202] minF1=f buy +f DG +f loss +f QV

[0203] The constraints are the same as those described in step S3.

[0204] Solution

[0205] The distribution network reactive power optimization model is regarded as a mixed integer second-order cone programming problem, and its expression is simplified as follows:

[0206] min cx

[0207] stAx≤b

[0208] Bx=d

[0209] ||Hx||2≤hx

[0210] Among them, c, A, b, B, d, H, and h constitute the correlation coefficient matrix of the objective function and the constraints, Ax≤b and Bx=d respectively constitute a series of inequality and equality constraints of the model, and ||Hx||2≤hx represents the second-order cone inequality constraint in the model.

[0211] The above model is solved by mathematical optimization solver, and finally the optimal reactive power optimization dispatching scheme is obtained. Through this method, the comprehensive operation cost of the distribution network can be minimized under the premise of meeting various constraints, and the reactive power support capacity of electric vehicle chargers can be fully utilized to improve the economy and stability of the distribution network.

[0212] Simulation settings and parameters

[0213] The example in this simulation is based on the modified IEEE-33 node distribution system for optimization analysis to explore the operation of the distribution network under the reactive support of charging piles. The system integrates a variety of equipment and management points, including 1 on-load tap-changing transformer, 3 sets of shunt capacitors, 2 distributed photovoltaics, 1 wind farm, and 2 EVAs, which are responsible for managing the charging of electric vehicles in the area. The example topology is as follows Figure 2 The topology of the example and the configuration of each device are as follows:

[0214] Root node and on-load tap-changing transformer: Node 1 is the root node of the system, connected to an on-load tap-changing transformer with 11 gears, with an adjustment range of [0.95,1.05]pu, and a maximum of 20 adjustments per day.

[0215] Distributed photovoltaic and wind farms: The photovoltaic units are connected to nodes 16 and 31, respectively, with a rated power of 0.65MW each; the wind farm is connected to node 14, with a rated power of 0.7MW.

[0216] Parallel capacitors: located at nodes 11, 17, and 31, the compensation capacity of each group is 10×50kvar, and each group can operate up to 10 times a day.

[0217] Electric vehicles and charging piles: Assume that each node (18 and 31) has 150 charging piles, which are managed by two EVAs respectively. The parameters of each electric vehicle include a battery rated capacity of 60kWh and a maximum charging and discharging power of 7kW, and the charging and discharging efficiency is 90%.

[0218] The typical daily charging and discharging demand and travel pattern of electric vehicles are predicted by Monte Carlo method, considering that the upper and lower SOC values ​​of the battery are 0.9 and 0.2. In addition, the daily load curve, time-of-use electricity price and output forecast of distributed new energy are drawn based on the data provided by the reference, revealing the relationship between the user's basic load and electricity price and the daily variation pattern of distributed new energy, ensuring the economic and efficient operation of the system.

[0219] In order to evaluate the effectiveness of the distribution network day-ahead dispatching strategy proposed in this invention, the following three scenarios are set up for simulation verification and analysis:

[0220] Scenario 1: Without considering on-load voltage regulation and capacitor switching equipment, electric vehicles consume electricity in the distribution network in a disorderly charging manner.

[0221] Scenario 2: Considering on-load voltage regulation and capacitor switching equipment, electric vehicles are charged in an orderly manner to consume electrical energy in the distribution network.

[0222] Scenario 3: Considering on-load voltage regulation and capacitor switching equipment, electric vehicles are charged in an orderly manner to consume the electric energy in the distribution network, and during the charging process, the charging piles provide reactive support to the grid.

[0223] Figure 3The power diagram of the electric vehicle cluster when charging in different nodes is shown. As can be seen from the figure, the disorderly charging load is mainly concentrated in the peak period of the user's basic load, resulting in the phenomenon of "peak on peak" in the distribution network load. In particular, the behavior of charging at the vulnerable end nodes of the system poses a threat to the stable operation of the system. Figure 4 The voltage curve of the concentrated access node of electric vehicles is shown. It can be observed that due to the access of charging load, the terminal node voltage, which was originally in a fragile state, dropped to below 0.86pu during the peak period of electricity consumption. Although in the period from 1:00 to 3:00 and 14:00 to 16:00, due to the relatively low system base load and the support of large wind and solar output, the node voltage remained above 0.95pu, but overall, the disorderly access of charging load seriously affected the voltage stability of the system, causing the voltage of the terminal node of the system to frequently exceed the limit, posing a severe challenge to the safe and stable operation of the distribution system.

[0224] Figure 5 The orderly charging power diagram of the electric vehicle cluster at different nodes in scenario 2 is shown. As can be seen from the figure, under the control of the dispatching center, the electric vehicle charging load is transferred to the early morning and the period when the system base load is low during the day, which coincides with the concentrated output period of wind power and distributed photovoltaic power generation, and can well absorb the new energy power generation, and feedback electricity to the power grid during the two base load peak periods, alleviating the power supply pressure of the power grid.

[0225] Figure 6 The diagram shows the orderly charging power and reactive power compensation power of the electric vehicle cluster at different nodes in scenario 3. Figure 5 By comparison, it can be seen that the charging and discharging power of the electric vehicle cluster in scenario three is slightly lower than that in scenario two, but the charging and discharging time periods are basically the same. This is because the electric vehicle cluster in scenario three needs to provide reactive power compensation to the power grid and transfer part of the charging power to other time periods, which leads to a decrease in its charging and discharging power.

[0226] Figures 7 to 9 The operation of the on-load tap-changing transformer (OLTC) and shunt capacitor (CB) in scenarios 2 and 3 is shown. In scenario 3, the electric vehicle cluster significantly reduces the action frequency and adjustment gear of OLTC and CB by providing reactive support. Specifically, the number of actions of OLTC is reduced from 11 times in scenario 2 to 6 times, and the number of actions of CB is reduced from 7 times to 2 times. In addition, the adjustment gear of OLTC and CB is also lower than that in scenario 2 most of the time, indicating that the reactive support of electric vehicles can effectively reduce the workload of traditional reactive / voltage regulating equipment, increase the capacity of the distribution network to accommodate the connection of more electric vehicles to the grid, and avoid unnecessary expansion of the distribution network due to disorderly grid connection.

[0227] In addition, whether it is scenario 2 or scenario 3, the OLTC operates more frequently and at a higher gear from 1:00 to 8:00. This is mainly due to the low time-of-use electricity price during this period, the window period of distributed photovoltaic output, and the insufficient wind power output, which leads to the superposition of charging load and basic load and the decrease of node voltage. Therefore, in order to maintain the voltage limit of the end node of the system, it is necessary to adjust the OLTC frequently. In the period from 13:00 to 17:00, due to the high output of distributed photovoltaics and the reduction of part of the charging power in scenario 3 to support reactive power regulation, electric vehicles not only help to absorb the output of new energy, but also reduce the need for OLTC to operate in high gear.

[0228] The access location of the shunt capacitor (CB) and whether the electric vehicle cluster provides reactive support to the grid have a significant impact on the switching operation of the CB. CB1 is located at node 11. According to the topology of the distribution system, this location can effectively realize reactive compensation on-site, increase the node voltage, and help stabilize the voltage changes of subsequent nodes. Therefore, in scenarios 2 and 3, the switching frequency of CB1 is high to maintain node voltage stability. CB2 is located at node 17. In scenario 3, the reactive support provided by the electric vehicle cluster reduces the frequent switching demand of CB2, thereby reducing the operating pressure of the equipment. CB3 is located at the end node 33 of the system. The voltage of this node is relatively fragile, and since no additional capacitors are installed at its proximal nodes, the reactive support of the electric vehicle cluster has little effect on CB3 and fails to significantly change its operating frequency or gear.

[0229] Fig.10 Compare the voltage changes after the electric vehicle cluster is connected to node 18 and node 33 in different scenarios. From the results, it can be seen that the voltage performance of scenarios 2 and 3 is better than that of scenario 1, and the voltage at each moment is maintained at 0.95pu above the safe operation standard. Although the voltage performance of node 18 in scenario 2 is slightly better than that of scenario 3, the combined action frequency of OLTC and CB shows that the traditional reactive / voltage regulating equipment in scenario 2 mostly operates in high gear, which limits the possibility of further increasing the charging load of electric vehicles. For node 33, the voltage performance of scenario 3 is generally better than that of scenario 2, especially from 13:00 to 15:00, the voltage of scenario 2 is slightly better because OLTC operates in high gear.

[0230] Table 1 shows the comprehensive operating cost comparison under different scenarios. Scenario 1 does not take into account the operating cost of capacitor banks and on-load tap-changing transformers, but because the disorderly charging of electric vehicle clusters causes the charging load to be concentrated during peak hours, the electricity purchase cost and network loss are significantly higher than other scenarios. After the introduction of orderly charging of electric vehicles and traditional voltage regulation / reactive power compensation devices in Scenario 2, although the operating costs of related equipment have increased, the significant reduction in electricity purchase and network loss costs has improved the overall economy. Scenario 3 further introduces electric vehicles to provide reactive support to the power grid through charging piles, optimizes the operation of capacitor banks and on-load tap-changing transformers, and thus further reduces network loss costs. Overall, it is the most economical option among the three scenarios.

[0231] Table 1 Comprehensive operating cost of the distribution network optimized before

[0232]

[0233] In view of the negative impact of disorderly access of electric vehicles on the operation of distribution network, this step models the dispatchable potential and charging and discharging demand of electric vehicle clusters, and proposes a hierarchical control strategy for charging and discharging of electric vehicles. On this basis, considering the reactive support capacity of charging piles, a day-ahead optimization scheduling model with the lowest comprehensive operation cost of active distribution network as the objective function is established to obtain the day-ahead active power of electric vehicle clusters, the gear position of on-load voltage-regulating transformers, the switching gear position of capacitor banks, and the day-ahead plan of the distribution network to purchase electricity from the upstream power grid. Considering the reactive support of charging piles can effectively reduce the number of actions and switching gears of traditional reactive / voltage-regulating equipment in the distribution network, reduce system network losses, and improve the economic efficiency of distribution network operation. The simulation experiment based on IEEE-33 node system verifies the effectiveness of the proposed day-ahead optimization scheduling model of active distribution network under reactive support of charging piles. From the simulation results, it can be seen that considering the reactive support of charging piles can effectively reduce the number of actions and switching gears of traditional reactive / voltage-regulating equipment in the distribution network, reduce system network losses, and improve the economic efficiency of distribution network operation.

[0234] The distribution network reactive power optimization device considering the reactive power support provided by the electric vehicle charger described below and the distribution network reactive power optimization method considering the reactive power support provided by the electric vehicle charger described above can be referred to each other.

[0235] Please refer to the attached Fig.11 The present invention also provides a distribution network reactive power optimization device that considers the reactive power support provided by the electric vehicle charger, including:

[0236] The data acquisition module 100 is used to obtain the distribution network operation data, electric vehicle charging data, and the parameter data of the on-load transformer and capacitor bank;

[0237] Prediction module 200, used to predict wind, solar and base load for the next day;

[0238] The optimization scheduling module 300 is used to construct a distribution network reactive power optimization scheduling model based on the parameters and prediction data, and generate a scheduling plan;

[0239] The control and communication module 400 is used to execute the scheduling plan generated by the optimization scheduling module and send specific charging and discharging instructions and reactive power support instructions to each electric vehicle.

[0240] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0241] Please refer to the attached Fig.12 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is executed.

[0242] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0243] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0244] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reactive power optimization method for distribution network considering reactive power support provided by electric vehicle chargers, characterized in that: The following steps are involved: Establish an electric vehicle operation model that considers the reactive power support provided by the electric vehicle charger, including its variables and constraints; Establish the operation model of the on-load tap-changing transformer and capacitor bank, including its variables and constraints; Based on the second-order cone relaxation algorithm, the power flow constraints of the distribution network are constructed, including active power and reactive power balance constraints and node voltage and branch current safety constraints; Based on the variables and constraints established above, taking the lowest comprehensive operation cost of the distribution network as the objective function, a distribution network reactive power optimization model and its solution method considering the reactive power support provided by electric vehicle chargers are constructed; The electric vehicle operation model includes: Define variables: Electric vehicle charging and discharging power variables and Charge and discharge state variables and Battery charge variable E m,t , charger active and reactive power variables and Based on the above variables and electric vehicle parameters, the electric vehicle operation model is constructed as follows: in, are the charging power, discharging power and rated charging and discharging power of electric vehicle m at time t, and are the charge and discharge states of electric vehicle m at time t, Equal to 1 indicates charging state, Equal to 1 indicates the discharge state, and The value of cannot be 1 at the same time. It is the active power flowing in both directions of the electric vehicle charger. Positive indicates charging, and negative indicates discharging. It is the reactive power flowing in both directions of the electric vehicle charger. A positive value indicates reactive power is released, and a negative value indicates reactive power is absorbed. is the apparent power of the electric vehicle charger, E m,t They represent the expected charge capacity, initial charge capacity and charge capacity of vehicle m at time t respectively; and They represent the upper and lower limits of the energy boundary during the charging and discharging process of vehicle m; η ch , η dis is the charging and discharging efficiency of the vehicle, and Δt is the time interval between two scheduling times.

2. The method for optimizing reactive power of distribution network considering reactive power support provided by electric vehicle chargers according to claim 1 is characterized in that: The on-load tap-changing transformer and capacitor bank operation model includes: Define the variables of the on-load transformer operation model: the action state variable o of the on-load tap changer k at time t k,t , the scalar value of the gear up and down of the on-load tap-changing transformer at time t and Based on the above variables and on-load transformer parameters, the on-load transformer operation model is constructed as follows: Among them, U 0,t is the square value of the voltage at the root node of the high-voltage side of the on-load tap-changing transformer at time t; a is the ratio difference of the adjacent gears of the on-load tap-changing transformer; o k,t Indicates the operating state of the on-load tap changer k at time t, which is a 0-1 variable; 0-1 marks respectively representing the gear up and down of the on-load tap-changing transformer at time t; Indicates the maximum range of change of the gear position of the on-load tap-changing transformer; Limit the number of operations of the on-load transformer in one day. The specific constraints are as follows: Among them, n oltc,max Indicates the maximum number of operations allowed for the on-load tap-changing transformer in one day; Define the variables of the capacitor bank operation model: c n,t Indicates the identification variable of the nth group of capacitors put into use at time t; are the identifier variables indicating the increase and decrease of the number of capacitor banks put into operation at time t; Based on the above variables and on-load transformer parameters, the capacitor bank operation model is constructed as follows: Among them, q cap Indicates the reactive power provided when a single group of capacitors is put into operation, c n,t The 0-1 identification variable represents the number of capacitors in the nth group at time t. 0-1 variables indicating the increase or decrease of the number of capacitor banks at time t, Indicates the maximum range of variation of the number of capacitor groups put into operation; Limit the number of operations of the capacitor bank in one day. The specific constraints are as follows: Among them, n CB,max Indicates the maximum number of operations allowed for the capacitor bank in one day.

3. The reactive power optimization method for distribution network considering reactive power support provided by electric vehicle chargers according to claim 1 is characterized in that: The construction of distribution network power flow constraints based on the second-order cone relaxation algorithm includes: Among them, β j represents the set of terminal nodes of the branch with node j as the first node, α j represents the set of the first-end nodes of the branch with node j as the terminal node, P ij,t and Q ij,t They represent the active power and reactive power transmitted by the branch at time t, respectively, ij and r ij They represent the line resistance and reactance of branch ij at time t, and They represent the active power and reactive power consumed by the conventional load of node j at time t, and They represent the active power and reactive power injected from the root node at time t, is the active power injected by the distributed generation at node j at time t, is the reactive power injected by the capacitor bank at node j at time t, I ij,t is the square value of the current flowing through branch ij at time t, that is, U i,t is the square value of the voltage at node i at time t, that is, Safety constraints on node voltage and branch current: Among them, u i,min 、u i,max are the upper and lower limits of the voltage amplitude allowed for node i, ij,max is the upper limit of the transmission current allowed in branch ij.

4. The reactive power optimization method for distribution network considering reactive power support provided by electric vehicle chargers according to claim 1 is characterized in that: The reactive power optimization model of the distribution network considering the reactive power support provided by the electric vehicle charger and its solution method include: The comprehensive operating cost of the active distribution network is taken as the objective function. The comprehensive operating cost mainly includes the distribution network power purchase cost, the penalty cost of wind and solar power abandonment, network loss cost, and reactive power / voltage control equipment adjustment cost: minF1=f buy +f DG +f loss +f QV Among them, f buy represents the system electricity purchase cost, f DG represents the penalty cost of abandoning wind and solar power, f loss represents the network loss cost, f QV represents the regulation cost of traditional reactive / voltage control equipment; The system power purchase cost expression is as follows: in, P represents the price of electricity purchased by the distribution network from the upstream power grid in the t period, grid,t It represents the active power transmitted from the upstream power grid to the distribution network in the tth period, T is the day-ahead dispatching time length, and Δt is the time interval; The penalty cost expression for wind and solar power abandonment is as follows: Among them, N DG represents the node set of distributed generation connected to the distribution network, c pen represents the unit power curtailment cost, represents the actual output of the distributed generation at node j during period t, represents the predicted output of the distributed generation at node j in period t; The network loss cost expression is as follows: Where L represents the set of transmission lines in the distribution network, c loss represents the unit power loss cost, represents the active power loss of branch ij in period t; The expression of reactive power / voltage control equipment regulation cost is as follows: Among them, N CB is the node set of the capacitor bank connected to the distribution network, c oltc 、c CB Respectively represent the cost of one-time operation of on-load tap-changing transformer and capacitor bank, n oltc 、n CB,j Respectively represent the number of operations of the load-regulating transformer and capacitor bank in one day; By using the reactive power optimization model of the distribution network that considers reactive power support provided by electric vehicle chargers, based on the wind and solar output and conventional load forecast data in a future dispatching cycle, combined with the traditional reactive power / voltage regulation equipment of the distribution network, the charging and discharging power of electric vehicles, the gear position of the on-load tap-changing transformer, the switching gear position of the capacitor bank, and the day-ahead plan of the distribution network to purchase electricity from the upstream power grid are optimized; The reactive power optimization model of the distribution network is regarded as a mixed integer second-order cone programming problem, and its expression can be simplified as follows: min cx st Ax≤b Bx=d ||Hx||2≤hx Among them, c, A, b, B, d, H, h constitute the correlation coefficient matrix of the objective function and the constraint conditions, Ax≤b and Bx=d constitute a series of inequality and equality constraints of the model respectively, and ||Hx||2≤hx represents the second-order cone inequality constraint in the model; Solved using a mathematical optimization solver.

5. A reactive power optimization device for a distribution network taking into account reactive power support provided by an electric vehicle charger, used to implement the method described in any one of claims 1 to 4, characterized in that it comprises: Data acquisition module, used to obtain distribution network operation data, electric vehicle charging data, and on-load transformer and capacitor bank parameter data; The prediction module is used to predict the wind, solar and base load for the next day; An optimization scheduling module is used to construct a distribution network reactive power optimization scheduling model based on the parameters and prediction data, and generate a scheduling plan; The control and communication module is used to execute the scheduling plan generated by the optimization scheduling module and issue specific charging and discharging instructions and reactive support instructions to each electric vehicle.

6. A computer device 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, the method according to any one of claims 1 to 4 is implemented.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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