EV-containing power distribution network voltage optimization method and system based on sparrow optimization algorithm

Abstract

The invention relates to an EV-containing power distribution network voltage optimization method and system based on a sparrow optimization algorithm, and the method comprises the following steps: obtaining the operation data of a power distribution network, respectively constructing an electric vehicle random load model and a power distribution network operation model, and enabling the power distribution network to comprise a photovoltaic vehicle and an electric vehicle; based on the electric vehicle random load model and the power distribution network operation model, establishing a power distribution network voltage control model considering the electric vehicle, wherein the power distribution network voltage control model takes the minimum power distribution network voltage offset and the minimum photovoltaic and electric vehicle regulation and control cost as targets; and solving the voltage control model of the power distribution network by adopting a sparrow optimization algorithm to obtain a power configuration scheme of the photovoltaic and electric vehicles, and completing the voltage optimization process of the power distribution network. Compared with the prior art, the method has the advantages of achieving the minimum voltage deviation, improving the economical efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network voltage optimization, and in particular to a distribution network voltage optimization method and system containing EVs based on a sparrow optimization algorithm. Background Art

[0002] With the rapid development of renewable energy technology and the increasing penetration of electric vehicles (EVs), modern distribution networks are undergoing significant changes. Photovoltaic (PV) systems and electric vehicles have become important components of distribution networks, providing energy supply and demand for the power grid, respectively. However, the existing technology mainly focuses on the distribution network system of photovoltaic and other power generation units, without considering the impact of electric vehicles. For example, patent application CN118249425A discloses an active distribution network voltage control method based on an improved sparrow search algorithm, in which the optimal control of the distribution network voltage is achieved only by collaboratively controlling the reactive output of each photovoltaic. For example, patent application CN116316636A discloses a microgrid optimization scheduling method based on a sparrow search algorithm, which also does not consider the impact of electric vehicles in the microgrid.

[0003] With the widespread popularity of electric vehicles, the irregular usage behavior of users has led to a huge change in the optimization and dispatch of microgrids. The voltage optimization of distribution networks containing photovoltaics and electric vehicles faces complex challenges due to the volatility of photovoltaic power sources and the uncertainty of electric vehicle charging demand. Traditional voltage control methods may not be sufficient to cope with the dynamic and random characteristics of these new energy elements. And their intermittent and unpredictable nature may lead to voltage fluctuations and power quality problems in distribution networks, so it is necessary to develop robust optimization techniques to ensure the stability and efficiency of the power grid. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for voltage optimization of a distribution network containing EVs based on a sparrow optimization algorithm to achieve voltage optimization of multiple collaborative participants under multiple objectives of the distribution network.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for optimizing voltage of a distribution network containing EVs based on a sparrow optimization algorithm comprises the following steps:

[0007] Obtaining distribution network operation data, and constructing an electric vehicle random load model and a distribution network operation model respectively, wherein the distribution network includes photovoltaics and electric vehicles;

[0008] Based on the electric vehicle random load model and the distribution network operation model, a distribution network voltage control model taking electric vehicles into account is established with the goal of minimizing the distribution network voltage deviation and minimizing the photovoltaic and electric vehicle regulation costs;

[0009] The distribution network voltage control model is solved by using the sparrow optimization algorithm to obtain the power configuration scheme of photovoltaic and electric vehicles, thus completing the distribution network voltage optimization process.

[0010] Furthermore, the electric vehicle random load model includes a driving distance probability density function, a charging time probability density function and a charging duration probability density function, and the driving distance probability density function is expressed as:

[0011]

[0012] The expression of the probability density function of the charging time is:

[0013]

[0014] The expression of the probability density function of the charging duration is:

[0015]

[0016] In the formula, f s (x) is the probability density of driving distance, x is the driving distance, σ s is the standard deviation of daily driving distance, μ s is the expected value of daily driving distance, f cf (t) is the probability density of the time when charging starts, μ D The expected time to start charging the electric vehicle, σ s is the standard deviation of the charging start time, t is the time point when charging starts, is the probability density of charging duration, P sc is the charging power of a single EV.

[0017] Furthermore, the distribution network operation model includes a distribution network voltage index and a regulation cost index, and the expression of the distribution network voltage index is:

[0018]

[0019] Where U level is the characteristic value of voltage fluctuation in the distribution network in a single cycle, T is the total time length of the calculation cycle statistics, N is the total number of nodes in the distribution network, and U i,t U0 and U1 are the voltage and standard voltage of the ith node at time t, respectively. i,max is the maximum deviation value allowed for the voltage of the i-th node;

[0020] The control cost index includes the reactive power control cost of photovoltaic power source and the compensation price of electric vehicle, wherein the expression of the reactive power control cost of photovoltaic power source is:

[0021]

[0022] In the formula, Q PV is the maximum adjustable reactive power capacity of the photovoltaic inverter, S PV is the rated capacity of the photovoltaic inverter, P PV is the active power of photovoltaic power generation, C PV is the reactive power output control cost, n is the number of photovoltaic cells connected to the distribution network, k PVc is the cost coefficient of unit reactive power regulation of the photovoltaic system, Q PV,t is the reactive power regulation amount performed by the PV inverter during the period t;

[0023] The expression of the electric vehicle compensation price is:

[0024]

[0025] In the formula, C EV Compensation price for electric vehicles, C com , C f and C c are the communication expenses, economic losses and the costs caused by reduced comfort, k EVc P is the compensation coefficient for subsidizing the user's unit charging and discharging power. EV,t is the charging and discharging power of the electric vehicle at time t, and N is the number of photovoltaic clusters connected to the distribution network.

[0026] Furthermore, the distribution network voltage control model includes an objective function and corresponding constraints, and the expression of the objective function is:

[0027] f=ω1U level +ω2(C PV +C EV )

[0028] The constraints include power flow equation constraints, photovoltaic power source reactive power constraints, and electric vehicle charging and discharging power constraints, wherein the power flow equation constraint is expressed as:

[0029]

[0030] The expression of the photovoltaic power source reactive output constraint is:

[0031] Q PVmin_i ≤Q PVi,t ≤Q PVmax_i

[0032] The expression of the electric vehicle charging and discharging power constraint is:

[0033]

[0034] In the formula, f is the objective function, ω1 and ω2 are weight values, and U level is the characteristic value of voltage fluctuation in the distribution network in a single cycle, C PV To control the cost of reactive power output, C EV is the compensation price for electric vehicles, P PVi , Q PVi are the active and reactive power outputs of the photovoltaic power source at node i, P Li , Q Li are respectively the active and reactive power of the traditional load on node i, P EVi , Q EVi are the active and reactive power outputs of the electric vehicles connected to node i, U i , U j are the voltage amplitudes of nodes i and j respectively, G ij , B ij ,θ ij are the real part, imaginary part and voltage phase difference of the admittance between node i and node j, Q PVmin_i , Q PVmax_i are the upper and lower limits of the reactive power adjustable capacity of the photovoltaic power source connected to node i, P EVci,t , P EVdi,t are the controllable charging power and discharging power of EV at node i respectively; P EVmin_ci , P EVmax_ci and P EVmin_di , P EVmax_di They are the maximum and minimum charging power, and the maximum and minimum discharging power that can be controlled by EV at node i, respectively.

[0035] Furthermore, the solution steps of the sparrow optimization algorithm include:

[0036] 1) Initialize the population size, number of iterations, and the ratio of discoverers, followers, and guards;

[0037] 2) applying the objective function to each individual sparrow in the population, calculating the individual fitness score of each individual sparrow, forming a fitness score set and sorting it, and determining the position corresponding to each individual sparrow;

[0038] 3) Updating the locations of the discoverer, follower and sentinel;

[0039] 4) Update the individual fitness score of each sparrow and re-determine the corresponding position;

[0040] 5) Repeat steps 2)-4) until the iteration termination condition is reached, and the optimal fitness score of the population is output as the optimal objective function value. The individual position of the sparrow corresponding to the optimal fitness score is used as the optimal target solution, that is, the power configuration scheme of photovoltaic and electric vehicles.

[0041] Further, the population is represented as:

[0042]

[0043] In the formula, E is the population, n is the number of sparrows in the population, d is the dimension of the sparrow individual, is the position information of the nth sparrow in the d dimension, which represents the power configuration of photovoltaic and electric vehicles in voltage optimization.

[0044] Furthermore, the expression of the discoverer position update is:

[0045]

[0046] Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-th dimension, α is a control parameter with a value between [-1,1], and M iter is the maximum number of iterations, R2 and ST are the alarm value and safety threshold respectively, Q is a random number that obeys the normal distribution, L is a 1×j-dimensional matrix, and all elements are 1.

[0047] Furthermore, the expression for updating the follower position is:

[0048]

[0049] Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-dimension, is the position of the individual with the worst fitness in the tth generation, Q is a random number that follows a normal distribution, is the position of the individual sparrow with the best fitness in the t+1th generation, A is a 1×j-dimensional matrix, and the matrix elements are random values ​​of 1 or -1, where A + =A T (AA T ) -1 , L is a 1×j dimensional matrix, and all elements are 1, and n is the number of sparrows in the population.

[0050] Furthermore, the expression for updating the position of the sentinel is:

[0051]

[0052] Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-dimension, is the global optimal position in the tth generation, β is the control step size, which obeys the normal distribution with mean 0 and variance 1, k is a random number between 0 and 1, ∈ is set to a constant, and f i is the fitness value of the current individual, f g 、f w are the fitness values ​​of the best and worst individuals in the current population respectively.

[0053] The present invention also provides a distribution network voltage optimization system including EVs based on a sparrow optimization algorithm, comprising:

[0054] Load model and operation model building module: used to obtain distribution network operation data, and respectively build electric vehicle random load model and distribution network operation model, wherein the distribution network includes photovoltaic and electric vehicles;

[0055] A distribution network voltage control model building module is used to establish a distribution network voltage control model taking electric vehicles into account with the goal of minimizing distribution network voltage deviation and minimizing photovoltaic and electric vehicle regulation costs based on the electric vehicle random load model and the distribution network operation model;

[0056] Solving module: used to solve the distribution network voltage control model using the sparrow optimization algorithm, obtain the power configuration scheme of photovoltaic and electric vehicles, and complete the distribution network voltage optimization process.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] (1) The present invention takes into account the impact of electric vehicles participating in the distribution network. By constructing an electric vehicle random load model to simulate the random output of electric vehicle loads, the present invention can effectively simulate the scenario of electric vehicles being connected to the distribution network, and establish a distribution network voltage control model taking electric vehicles into account in combination with the distribution network operation model. The voltage control model aims to minimize voltage deviation and minimize photovoltaic and electric vehicle regulation costs, and adopts the sparrow optimization algorithm for solving the problem, thereby achieving voltage optimization of multiple collaborative participants under multiple objectives of the distribution network.

[0059] (2) The present invention adopts the sparrow optimization algorithm to solve the distribution network voltage control model, which can coordinate the control of the optimal reactive power of each photovoltaic and the optimal active power of the electric vehicle, effectively manage the uncertainty of photovoltaic power generation and electric vehicle charging, achieve global optimization, solve the voltage over-limit problem, and improve the overall control effect of the distribution network voltage.

[0060] (3) The distribution network voltage control model of the present invention takes the minimum voltage deviation of the distribution network and the minimum regulation cost of photovoltaic and electric vehicles as the optimization goals, taking into account both the minimization of voltage deviation and the minimization of regulation cost, achieving the minimum voltage deviation and the minimum regulation cost, and improving the reliability and economy of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0062] Figure 2 A flow chart for solving the distribution network voltage control model of the present invention;

[0063] Figure 3 It is the IEEE33 node distribution network structure diagram of the present invention;

[0064] Figure 4 The photovoltaic 24-hour output curve diagram of the present invention;

[0065] Figure 5 is the system load curve of the present invention;

[0066] Figure 6 It is a fitness comparison curve diagram of the present invention;

[0067] Figure 7 The voltage of the key node when the photovoltaic power generation of the present invention is normal;

[0068] Figure 8 The key node voltage obtained by using the particle swarm algorithm of the present invention;

[0069] Fig. 9 It is the key node voltage obtained by adopting the PSO algorithm of the present invention. DETAILED DESCRIPTION

[0070] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0071] Example 1

[0072] This embodiment aims at the voltage over-limit problem caused by the access of distributed photovoltaic power to the distribution network, and provides a distribution network voltage optimization method containing EVs based on the Sparrow Optimization Algorithm. The method proposes a distribution network voltage control model that takes photovoltaic power and electric vehicles into account, and uses the Sparrow Optimization Algorithm to solve it. First, considering the user demand response and voltage deviation index of electric vehicles, a distribution network voltage control model is established with the goal of minimizing the voltage deviation of the distribution network and minimizing the regulation cost of photovoltaic power and electric vehicles. Then, the model is solved by the Sparrow Optimization Algorithm to obtain the optimal configuration of photovoltaic reactive power and electric vehicle active power. Finally, an example analysis is performed through the IEEE33 node system. The results show that the Sparrow Optimization Algorithm can be used to effectively solve the photovoltaic reactive power and electric vehicle active power configuration scheme that minimizes the voltage fluctuation of the distribution network under the optimal regulation cost, proving that the Sparrow Optimization Algorithm has a faster convergence speed and better distribution characteristics in solving the distribution network voltage control model. Figure 1 As shown, the method specifically comprises the following steps:

[0073] S1. Electric vehicle load and distribution network operation model.

[0074] S1.1. Electric vehicle random load model.

[0075] In this study, it is assumed that electric vehicles can respond to the dispatching needs of the power grid and exchange energy with the power system through V2G technology when the grid needs additional power. Considering the large uncertainty in the driving distance, charging requirements and grid connection timing of electric vehicles, the management strategy should first ensure that the travel needs of users are met.

[0076] By analyzing the data of household car usage patterns, it is found that the daily mileage follows a log-normal distribution. Therefore, it can be inferred that the daily mileage s of electric car users roughly conforms to the probability density function described by equation (1).

[0077]

[0078] Where: μ s is the expected value of daily driving distance, which is 3.2; s is the standard deviation of daily driving distance, which is 0.88.

[0079] The probability density function f of the time when the user starts charging cf (t) is described according to formula (2), and the probability distribution density function of charging duration is It is described according to formula (3).

[0080]

[0081] In the formula, μ DThe expected time for the electric vehicle to start charging is set to 17.6; σ s is the standard deviation of the charging start time, which is 3.4; t is the time point when charging starts.

[0082]

[0083] Where P sc is the charging power of a single EV.

[0084] Since each user’s driving parameters are independent and unrelated, we can use Monte Carlo simulation sampling technology to capture the statistical laws of these behaviors and predict the power distribution required by EVs throughout the day.

[0085] By constructing a load model for electric vehicles (EVs) and incorporating the load demand under the demand response strategy (DR), the grid dispatch center can accurately calculate the EV charging or discharging power that can be adjusted in each time period.

[0086] S1.2. Distribution network operation model.

[0087] S1.2.1. Distribution network voltage indicators.

[0088] Among the many criteria for evaluating the stability of distribution networks, the stability of node voltage is a crucial indicator. Assume that the characteristic value of voltage fluctuation in a single cycle of the distribution network is U level , the calculation formula is as follows:

[0089]

[0090] Where: T is the total time length of the calculation cycle statistics; N is the total number of nodes in the distribution network; U i,t U0 and U1 are the voltage and standard voltage of the ith node at time t respectively; ΔU i,max is the maximum allowed deviation of the voltage at the i-th node.

[0091] S1.2.2. Adjust cost indicators.

[0092] (1) Reactive power control cost of photovoltaic power source

[0093] The photovoltaic power generation equipment in the system can provide reactive power support for the distribution network because its active power output generally does not reach the maximum rated power of the photovoltaic inverter. Therefore, there is a certain relationship between the reactive power capacity that the photovoltaic inverter can adjust and its rated capacity, which can be expressed as:

[0094]

[0095] Where: Q PVis the maximum adjustable reactive power capacity of the photovoltaic inverter; S PV is the rated capacity of the photovoltaic inverter; P PV is the active power of photovoltaic power generation.

[0096] Considering that the photovoltaic power generation system usually cannot reduce its active power output when it is connected to the grid, we should use the photovoltaic inverter as a controllable reactive power source to participate in the voltage regulation work. The reactive power control cost C PV It is expressed as follows:

[0097]

[0098] Where: k PVc Q is the cost coefficient of unit reactive power regulation of the photovoltaic system, which is 0.32 yuan / kvar in this embodiment; PV,t is the reactive power regulation amount performed by the photovoltaic inverter during the period t; n is the number of photovoltaic cells connected to the distribution network.

[0099] (2) Electric vehicle compensation price

[0100] If an electric vehicle is charged or discharged at a constant power level within a specific period of time, a mathematical model can be established as shown in formula (7):

[0101]

[0102] Where: C com , C f and C c are the costs of communication expenses, economic losses and reduced comfort; k EVc The compensation coefficient for subsidizing the user's unit charging and discharging power is set to 0.473 yuan / kW in this embodiment; P EV,t is the charging and discharging power of the electric vehicle at time t; N is the number of photovoltaic clusters connected to the distribution network.

[0103] S2. Distribution network voltage control model considering electric vehicles.

[0104] S2.1. Objective function.

[0105] In this work, in order to comprehensively consider the economic efficiency of photovoltaic system construction and electric vehicles and the stability of distribution network operation, by constructing the key indicators of the distribution network and the load model of electric vehicles, the reactive power output of photovoltaics and the charging and discharging strategy of electric vehicles within the dispatching period are further optimized. Therefore, the objective function as shown in formula (8) can be considered:

[0106] f=ω1U level +ω2(C PV +C EV ) (8)

[0107] Where: ω1, ω2 are weight values.

[0108] S2.2. Constraints.

[0109] (1) Equality constraint, i.e., power flow equation constraint

[0110]

[0111] Where: P PVi , Q PVi are the active and reactive power outputs of the photovoltaic power source at node i; P Li , Q Li are respectively the active and reactive power of the traditional load on node i; P EVi , Q EVi are the active and reactive power outputs of the electric vehicles connected to node i; U i , U j are the voltage amplitudes of nodes i and j respectively; G ij , B ij ,θ ij are the real part, imaginary part and voltage phase difference of the admittance between node i and node j respectively.

[0112] (2) Reactive power output constraints of photovoltaic power sources

[0113]

[0114] In the formula, Q PVmin_i , Q PVmax_i They are respectively the upper and lower limits of the reactive adjustable capacity of the photovoltaic power source connected to node i.

[0115] (3) Electric vehicle charging and discharging power constraints

[0116]

[0117] Where P EVci,t , P EVdi,t are the controllable charging power and discharging power of EV at node i respectively; P EVmin_ci , P EVmax_ci and P EVmin_di , P EVmax_di They are the maximum and minimum charging power, and the maximum and minimum discharging power that can be controlled by EV at node i, respectively.

[0118] S3, SSA algorithm solution.

[0119] The sparrow optimization algorithm (SSA) is a novel intelligent optimization method developed based on the sparrow's foraging behavior and natural behavior of escaping foragers. It is widely used in solving optimization problems.

[0120] S3.1. Algorithm principle.

[0121] In the sparrow search algorithm, individuals are divided into discoverers, followers, and guards, and each individual position corresponds to a solution. According to the algorithm settings, the proportion of guards in the population is 10% to 20%, and the discoverers and followers are dynamically changing, that is, if one individual becomes a discoverer, it means that another individual will become a follower. According to the division of labor, the discoverer mainly provides foraging directions and areas for the entire population, the follower follows the discoverer to forage, and the guard is responsible for monitoring the foraging area. In the foraging process, resources are acquired by constantly updating the positions of the three.

[0122] Assume that there are n sparrows in the population, then the population composed of all individuals can be expressed as E:

[0123]

[0124] Where: n is the number of individuals in the population; d is the individual dimension; is the position information of the nth sparrow in the d dimension.

[0125] (1) Discoverer location update

[0126]

[0127] Where: t is the current iteration number, is the position of the i-th sparrow in the t-th generation in the j-th dimension; α is a control parameter, with a value between [-1,1]; M iter is the maximum number of iterations; R2 and ST are the alarm value and safety threshold respectively; Q is a random number that obeys the normal distribution; L is a 1×j-dimensional matrix, and all elements are 1.

[0128] (2) Follower location update

[0129]

[0130] Where: is the position of the individual with the worst fitness in the tth generation; is the position of the individual with the best fitness in the t+1th generation; A is a 1×j-dimensional matrix, and the matrix elements are random values ​​of 1 or -1, where A + =A T (AA T ) -1 .

[0131] (3) Update of the position of the alerter

[0132]

[0133] Where: is the global optimal position in the tth generation; β is the control step size, which obeys the normal distribution with mean 0 and variance 1; k is a random number between 0 and 1; ∈ is set to a constant to avoid the denominator being 0; f i is the fitness value of the current individual, f g 、f w are the fitness values ​​of the best and worst individuals in the current population respectively.

[0134] The basic steps of the sparrow optimization algorithm are as follows:

[0135] (1) Initialize the population size, number of iterations, and the ratio of discoverers, followers, and sentinels.

[0136] (2) Apply the objective function to each member of the group to determine its fitness score and form a fitness score set, which is then sorted.

[0137] (4) Update the discoverer position, follower position and sentinel position according to equations (13), (14) and (15).

[0138] (5) Calculate the fitness value and update the individual position.

[0139] (6) Termination condition: When the predetermined number of iterations is reached or the specific optimization accuracy requirement is met, further calculations are terminated.

[0140] S3.2. Distribution network voltage optimization process based on sparrow optimization algorithm.

[0141] The sparrow optimization algorithm is implemented using MATLAB language and applied to the distribution network voltage control model established in this embodiment. The solution process is as follows: Figure 2 The control variables include the reactive power provided by the photovoltaic power source and the charging and discharging power of the electric vehicle in a single cycle.

[0142] S4. Example analysis.

[0143] S4.1. Basic data.

[0144] This example uses the IEEE-33 node power distribution system as the example research object. The network structure is detailed in Figure 3 The power flow calculation is implemented using the forward-backward substitution technology. The base capacity of the system is 0.315MVA, the base voltage is 0.38kV, the maximum voltage deviation allowed for normal operation of the distribution network is set to ±7%, and the root node voltage is 1.07pu. The photovoltaic access nodes 3, 10 and 17 are set, and the rated capacity of the photovoltaic inverter is set to 0.3MVA. The 24-hour photovoltaic output curve is as follows: Figure 4As shown, the electric vehicle cluster is set to access node 15 and node 32, the rated capacity is set to 0.45MW and 0.36MW, and the electric vehicle charging power and discharging power are set to 4kW.

[0145] S4.2. Simulation analysis.

[0146] In this embodiment, the Monte Carlo simulation technology is used to predict the electric vehicle load pattern in 24 different time periods of a day and integrate the comprehensive effect of electric vehicles connected to the power grid. Figure 5 The depicted conventional load curve is subjected to data analysis to construct a full-day load curve.

[0147] Considering that the connection of photovoltaic power generation system to the power grid may cause the voltage at the end of the distribution line and the photovoltaic access location to exceed the limit, this embodiment specially selects nodes 10 and 18 where voltage is prone to exceed the limit for research, and uses node 3 close to the power grid as a reference point to compare and explain the phenomenon of voltage exceeding the limit. In order to verify the effectiveness of the sparrow optimization algorithm in solving the model proposed in this embodiment, the particle swarm algorithm mentioned in the existing literature is used for comparative analysis, and the obtained fitness curve is as follows Figure 6 shown.

[0148] from Figure 6 It can be seen that the particle swarm optimization algorithm requires more than 400 iterations to find the optimal solution. In comparison, the sparrow optimization algorithm can find the optimal solution in less than 100 iterations, and the optimal solution found is better, indicating that the sparrow optimization algorithm has a faster convergence speed and convergence accuracy in solving the distribution network voltage control model of this embodiment.

[0149] The key node voltage when the photovoltaic power is in normal output and the key node voltage optimization results obtained after solving the model using the particle swarm optimization algorithm and the sparrow optimization algorithm are shown in the figure. Figure 7 , Figure 8 and Fig. 9 shown.

[0150] Depend on Figure 7 It can be seen that the voltage exceeds the limit at noon and evening when the photovoltaic power is normally output. This is because the photovoltaic power output is too high at noon, exceeding the required load, and the photovoltaic power is not output at night when the load is at its peak. Figure 8 and Fig. 9 It can be seen that the optimal solutions obtained by the two algorithms can both return the voltage to the normal range, but the voltage deviation obtained by the sparrow optimization algorithm is smaller.

[0151] In the case of joint voltage control of electric vehicles (EVs) and PV power sources, the optimization results without considering the regulation costs of PV and EVs are compared with the optimization results with the goal of minimizing the cost of controllable resources in the system, as shown in Table 1.

[0152] Table 1 Costs of different algorithms

[0153]

[0154] It can be seen from Table 1 that the total cost obtained by solving the model using the sparrow optimization algorithm is 80.07 yuan less than the total cost obtained by solving the model using the particle swarm algorithm. Moreover, when the sparrow optimization algorithm is used to solve the model and the adjustment cost is considered in the optimization process, the total cost is saved by 280.02 yuan compared with the optimization case where the adjustment cost is ignored. This verifies the effectiveness of the model proposed in this embodiment, helps to improve the economic efficiency of the distribution network, and demonstrates the high-precision convergence of the sparrow optimization algorithm in solving the model in this embodiment.

[0155] In summary, this embodiment proposes a distribution network optimization model that takes into account the control costs of photovoltaics and electric vehicles and the minimum voltage deviation of the distribution network, and applies the sparrow optimization algorithm to the model of this embodiment. The conclusions are summarized as follows:

[0156] (1) The model established by this embodiment combined with the sparrow optimization algorithm can optimize the reactive power configuration of the photovoltaic system and the charging and discharging plan of the electric vehicle; while considering the photovoltaic reactive power output constraints and the electric vehicle charging and discharging constraints, it effectively improves the voltage distribution and improves the system economy.

[0157] (2) The sparrow optimization algorithm discussed in this embodiment is applicable to the voltage optimization problem of distribution networks involving photovoltaic and electric vehicles. Through comparative analysis with the particle swarm algorithm, it is confirmed that the sparrow optimization not only converges faster but also has better convergence performance when solving high-dimensional nonlinear problems. In addition, when pursuing the optimal solution to minimize the voltage deviation of the distribution network, the sparrow optimization can also effectively reduce the control cost.

[0158] Example 2

[0159] This embodiment provides a distribution network voltage optimization system including EVs based on a sparrow optimization algorithm, including:

[0160] Load model and operation model building module: used to obtain distribution network operation data, and respectively build electric vehicle random load model and distribution network operation model, wherein the distribution network includes photovoltaic and electric vehicles;

[0161] A distribution network voltage control model building module is used to establish a distribution network voltage control model taking electric vehicles into account with the goal of minimizing distribution network voltage deviation and minimizing photovoltaic and electric vehicle regulation costs based on the electric vehicle random load model and the distribution network operation model;

[0162] Solving module: used to solve the distribution network voltage control model using the sparrow optimization algorithm, obtain the power configuration scheme of photovoltaic and electric vehicles, and complete the distribution network voltage optimization process.

[0163] The rest is the same as in Example 1.

[0164] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0165] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0166] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing voltage of a distribution network containing EVs based on a sparrow optimization algorithm, characterized in that: The following steps are involved: Obtaining distribution network operation data, and constructing an electric vehicle random load model and a distribution network operation model respectively, wherein the distribution network includes photovoltaics and electric vehicles; Based on the electric vehicle random load model and the distribution network operation model, a distribution network voltage control model taking electric vehicles into account is established with the goal of minimizing the distribution network voltage deviation and minimizing the photovoltaic and electric vehicle regulation costs; The distribution network voltage control model is solved by using the sparrow optimization algorithm to obtain the power configuration scheme of photovoltaic and electric vehicles, thus completing the distribution network voltage optimization process.

2. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 1, characterized in that: The electric vehicle random load model includes a driving distance probability density function, a charging time probability density function, and a charging duration probability density function. The driving distance probability density function is expressed as: The expression of the probability density function of the charging time is: The expression of the probability density function of the charging duration is: In the formula, f s (x) is the probability density of driving distance, x is the driving distance, σ s is the standard deviation of daily driving distance, μ s is the expected value of daily driving distance, f cf (t) is the probability density of the time when charging starts, μ D The expected time to start charging the electric vehicle, σ s is the standard deviation of the charging start time, t is the time point when charging starts, is the probability density of charging duration, P sc is the charging power of a single EV.

3. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 1, characterized in that: The distribution network operation model includes a distribution network voltage index and a regulation cost index. The expression of the distribution network voltage index is: Where U level is the characteristic value of voltage fluctuation in the distribution network in a single cycle, T is the total time length of the calculation cycle statistics, N is the total number of nodes in the distribution network, and U i,t U0 and U1 are the voltage and standard voltage of the ith node at time t, respectively. i,max is the maximum deviation value allowed for the voltage of the i-th node; The control cost index includes the reactive power control cost of photovoltaic power source and the compensation price of electric vehicle, wherein the expression of the reactive power control cost of photovoltaic power source is: In the formula, Q PV is the maximum adjustable reactive power capacity of the photovoltaic inverter, S PV is the rated capacity of the photovoltaic inverter, P PV is the active power of photovoltaic power generation, C PV is the reactive power output control cost, n is the number of photovoltaic cells connected to the distribution network, k PVc is the cost coefficient of unit reactive power regulation of the photovoltaic system, Q PV,t is the reactive power regulation amount performed by the PV inverter during the period t; The expression of the electric vehicle compensation price is: In the formula, C EV Compensation price for electric vehicles, C com , C f and C c are the communication expenses, economic losses and the costs caused by reduced comfort, k EVc P is the compensation coefficient for subsidizing the user's unit charging and discharging power. EV,t is the charging and discharging power of the electric vehicle at time t, and N is the number of photovoltaic clusters connected to the distribution network.

4. The method for optimizing voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 1, characterized in that: The distribution network voltage control model includes an objective function and corresponding constraints. The expression of the objective function is: f=ω1U level +ω2(C PV +C EV ) The constraints include power flow equation constraints, photovoltaic power source reactive output constraints, and electric vehicle charging and discharging power constraints, wherein the power flow equation constraint is expressed as: The expression of the photovoltaic power source reactive output constraint is: Q PVmin_i ≤Q PVi,t ≤Q PVmax_i The expression of the electric vehicle charging and discharging power constraint is: In the formula, f is the objective function, ω1 and ω2 are weight values, and U level is the characteristic value of voltage fluctuation in the distribution network in a single cycle, C PV To control the cost of reactive power output, C EV is the electric vehicle compensation price, P PVi , Q PVi are the active and reactive power outputs of the photovoltaic power source at node i, P Li , Q Li are respectively the active and reactive power of the traditional load on node i, P EVi , Q EVi are the active and reactive power outputs of the electric vehicles connected to node i, U i , U j are the voltage amplitudes of nodes i and j respectively, G ij , B ij ,θ ij are the real part, imaginary part and voltage phase difference of the admittance between node i and node j, Q PVmin_i , Q PVmax_i are the upper and lower limits of the reactive power adjustable capacity of the photovoltaic power source connected to node i, P EVci,t , P EVdi,t are the controllable charging power and discharging power of EV at node i respectively; P EVmin_ci , P EVmax_ci and P EVmin_di , P EVmax_di They are the maximum and minimum charging power, and the maximum and minimum discharging power that can be controlled by EV at node i, respectively.

5. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 4, characterized in that: The solution steps of the sparrow optimization algorithm include: 1) Initialize the population size, number of iterations, and the ratio of discoverers, followers, and guards; 2) applying the objective function to each individual sparrow in the population, calculating the individual fitness score of each individual sparrow, forming a fitness score set and sorting it, and determining the position corresponding to each individual sparrow; 3) Updating the locations of the discoverer, follower and sentinel; 4) Update the individual fitness score of each sparrow and re-determine the corresponding position; 5) Repeat steps 2)-4) until the iteration termination condition is reached, and the optimal fitness score of the population is output as the optimal objective function value. The individual position of the sparrow corresponding to the optimal fitness score is taken as the optimal target solution, that is, the power configuration scheme of photovoltaic and electric vehicles.

6. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 5, characterized in that: The population is represented by: In the formula, E is the population, n is the number of sparrows in the population, d is the dimension of the sparrow individual, is the position information of the nth sparrow in the d dimension, which represents the power configuration of photovoltaic and electric vehicles in voltage optimization.

7. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 5, characterized in that: The expression for updating the finder's position is: Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-th dimension, α is a control parameter with a value between [-1,1], and M iter is the maximum number of iterations, R2 and ST are the alarm value and safety threshold respectively, Q is a random number that obeys the normal distribution, L is a 1×j-dimensional matrix, and all elements are 1.

8. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 5, characterized in that: The expression for the follower position update is: Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-dimension, is the position of the individual with the worst fitness in the tth generation, Q is a random number that follows a normal distribution, is the position of the individual sparrow with the best fitness in the t+1th generation, A is a 1×j-dimensional matrix, and the matrix elements are random values ​​of 1 or -1, where A + =A T (AA T ) -1 , L is a 1×j dimensional matrix, and all elements are 1, and n is the number of sparrows in the population.

9. The method for optimizing the voltage of a distribution network containing EVs based on the sparrow optimization algorithm according to claim 5, characterized in that: The expression for updating the position of the sentinel is: Where t is the current iteration number, are the positions of the i-th sparrow in the t-th generation and the t+1-th generation in the j-dimension, is the global optimal position in the tth generation, β is the control step size, which obeys the normal distribution with mean 0 and variance 1, k is a random number between 0 and 1, ∈ is set to a constant, and f i is the fitness value of the current individual, which is the comprehensive value of voltage deviation and compensation price, f g 、f w are the fitness values ​​of the best and worst individuals in the current population respectively.

10. A distribution network voltage optimization system containing EVs based on the sparrow optimization algorithm, characterized in that: include: Load model and operation model building module: used to obtain distribution network operation data, and respectively build electric vehicle random load model and distribution network operation model, wherein the distribution network includes photovoltaic and electric vehicles; A distribution network voltage control model building module is used to establish a distribution network voltage control model taking electric vehicles into account with the goal of minimizing distribution network voltage deviation and minimizing photovoltaic and electric vehicle regulation costs based on the electric vehicle random load model and the distribution network operation model; Solving module: used to solve the distribution network voltage control model using the sparrow optimization algorithm, obtain the power configuration scheme of photovoltaic and electric vehicles, and complete the distribution network voltage optimization process.

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