Active power distribution grid meshing method, system and medium considering distributed photovoltaic and electric vehicle

Abstract

This application provides an active distribution network gridding method, system, and medium considering distributed photovoltaic (PV) power and electric vehicles (EVs). The method includes the following specific steps: S1: Modeling uncertainties and constructing probability models for distributed PV and EVs; S2: Using a bacterial foraging algorithm to improve the particle swarm search formula, aiming to maximize the PV absorption rate and minimize line losses, to achieve network reconfiguration optimization and re-grid partitioning; S3: Validating the method in an IEEE 94-node system that includes distributed PV and EVs. This application improves the overall peak-shaving efficiency of the system and provides an effective method for coordinated scheduling decisions of multi-energy systems.

Description

Technical Field

[0001] This application relates to the field of distributed energy operation, specifically an active distribution gridding method, system, and medium that considers distributed photovoltaic and electric vehicles. Background Technology

[0002] With the large-scale integration of distributed generation (DG) and electric vehicles (EVs), the "source and load" of active distribution networks exhibits uncontrollable volatility and uncertainty. Traditional passive gridding methods are no longer adequate to address the impact of this volatility and uncertainty on distribution network operation and maintenance, necessitating new gridding methods to adapt to the rapid development of active distribution networks. Currently, distribution networks are undergoing a profound transformation from passive compliance to active management. Unlike passive distribution networks, active distribution networks are increasingly complex, requiring more refined gridding management. Traditional distribution networks often employ an open-loop radial operation mode, which is simple in structure, easy to manage, and relatively easy to divide into grids. With the rapid development of distributed generation (DG) and electric vehicles (EVs), passive distribution networks are gradually transforming into active distribution networks (ADNs) with active control and management functions. Due to the dual volatility and uncertainty of distributed generation and electric vehicles, significant uncertainties will emerge on both the "source and load" sides of active distribution networks. Traditional grid-based partitioning aims to reduce network losses and improve load balance, but it cannot cope with the uncertainties of the superposition of source and load in active distribution networks. Therefore, a new dynamic grid-based method is needed to flexibly deal with such uncertainties and achieve efficient operation and maintenance management of active distribution networks. Summary of the Invention

[0003] The purpose of this application is to provide an active distribution network gridding method, system, and medium that considers distributed photovoltaic and electric vehicles, so as to flexibly respond to uncertainties and achieve efficient operation and maintenance management of the active distribution network.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] In a first aspect, embodiments of this application provide an active distribution network gridding method considering distributed photovoltaic power and electric vehicles, comprising the following specific steps:

[0006] S1: Model the uncertainty and construct the uncertainty probability model for distributed photovoltaics and electric vehicles;

[0007] S2: With the goal of maximizing the distributed photovoltaic absorption rate and minimizing line loss, the particle swarm search formula is improved by using the bacterial foraging algorithm to achieve network reconstruction optimization and re-divide the grid.

[0008] S3: Validation in an IEEE 94-node system that includes distributed photovoltaics and electric vehicles.

[0009] The specific steps for modeling uncertainty and constructing an uncertainty probability model for distributed photovoltaics and electric vehicles are as follows:

[0010] The Wasserstein probability index model is adopted as the uncertainty output model for distributed photovoltaic power. The continuous probability density function is transformed into a discrete value with probability characteristics. The probability density functions of photovoltaic and wind power are used to generate output values ​​with probability characteristics at a certain moment through the Wasserstein probability index. A set of scenarios containing more moments is generated by connecting them through Cartesian products.

[0011] The formula for the quantiles of the Wasserstein probability index is:

[0012] (1)

[0013] In the formula: g(x) is the probability density function of the random variable; r is the order; c is the c-th quantile; C is the number of optimal quantiles.

[0014] The long-term statistical regularity of the randomness and uncertainty of solar irradiance conforms to a Beta function, and solar power output is linearly related to solar irradiance; therefore, solar power output approximately follows a Beta distribution.

[0015] (2)

[0016] In the formula: Ppv is the output power; Pmax is the extreme value of the output power; Let be the gamma function; α and β are the shape parameters of the Beta function.

[0017] Solving using formula (1), we get:

[0018] (3)

[0019] Secondly, in the electric vehicle output model, the charging power is a continuous probability density function of the charging start time and the charging duration.

[0020] The data patterns of charging duration and start time approximate a normal distribution. Using the Wasserstein probability index formula (1), we can obtain...

[0021] (4)

[0022] μ represents the mean of the distribution, and σ represents the standard deviation.

[0023] The goal is to maximize the distributed photovoltaic (PV) grid integration rate and minimize line losses. Specifically, a bacterial foraging algorithm is used to improve the particle swarm search formula, achieving network reconstruction optimization and re-dividing the mesh.

[0024] 1. Distributed Generator Utilization Rate: This indicator is defined as the ratio of the actual output of distributed generators to the real-time load of the distribution network. The average distributed generator utilization rate refers to the average utilization level of all distributed generators in the distribution network, which better reflects the supply capacity of distributed generators. The higher the defined average distributed generator utilization rate, the better. The expression is:

[0025] (5)

[0026] (6)

[0027] λ pvi P represents the grid connection efficiency of the i-th distributed photovoltaic system. pvi Let be the active power absorbed by the i-th distributed photovoltaic system; Let be the installed capacity of the i-th distributed photovoltaic system; is the average grid absorption rate; n is the total number of distributed photovoltaic (PV) systems in the grid.

[0028] Line loss: Line loss is an important indicator reflecting the economic efficiency of a power grid. While ensuring the safe operation of the system, line loss should be minimized as much as possible. The expression is:

[0029] (7)

[0030] Total power grid line losses; I i Let be the current in branch i; Ri be the resistance in branch i; n1 be the number of branches in the power grid; S be the current in branch i; b For branch circuit b, the switch status is 1 when closed and 0 when open.

[0031] 3. Constraints:

[0032] (1) Power balance constraint:

[0033] (8)

[0034] Among them, P i P represents the active power of the i-th node. pvi P represents the active power of the distributed power source at the i-th node; Li V represents the active power of the load at the i-th node; i V j Let Y be the voltage at nodes i and j; Y is the branch admittance matrix.

[0035] Voltage constraint:

[0036] (9)

[0037] in, , Let be the lower and upper limits of the allowable voltage for the i-th node.

[0038] Branch current constraints:

[0039] (10)

[0040] in, , Let these be the lower and upper limits of the current in branch b.

[0041] Active power output constraints of distributed generation:

[0042] (11)

[0043] in, , Let these be the lower and upper limits of the allowable active power output of the distributed photovoltaic system at the i-th node.

[0044] Switching action count constraint:

[0045] (12)

[0046] Where N is the total number of switches; , Switch i: Number of actions per day, maximum allowed number of actions; This represents the total number of allowed actions of the switch.

[0047] 4. Dynamic optimization and reconstruction:

[0048] The particle swarm optimization algorithm is improved by employing a bacterial foraging algorithm. The bacterial foraging algorithm mainly includes three steps: attraction, replication, and migration. Let represent the position of the i-th bacterium after the j-th directional operation, the k-th reproductive operation, and the l-th migration operation, Δ(i) be a unit vector in a random direction, and C(i) be the swimming step size of the i-th bacterium. By improving the swimming step size C(i) of the bacteria, the algorithm's computational accuracy is improved while ensuring the algorithm's convergence speed, allowing the algorithm to escape local minima, as shown in equations (13)-(15):

[0049] (13)

[0050] (14)

[0051] (15)

[0052] (16)

[0053] Where C max and C minThese represent the maximum and minimum step sizes, respectively, where t is the current iteration number. max f is the maximum number of iterations. rwij Let x be the coordinates of the random step size position. ij v represents the current position of particle i. ij It is the current flight speed of particle i. The inertia weight coefficient; t represents the iteration number; p ij p represents the best historical position that particle i has experienced so far; gj It is the best position found by the current particle swarm search; c1 and c2 are learning factors, called cognitive learning factors and social learning factors, respectively, taking values ​​between 0 and 2; r0, r1, and r2 are three independent random numbers on [0, 1]; ||*|| is the Euclidean norm to avoid excessive position update due to excessive vector length.

[0054] The improved particle swarm optimization algorithm based on the bacterial foraging algorithm is specifically as follows:

[0055] Step 1: Initialize parameters M and N ed N re N d N s , P ed Where M is the bacterial population size; N ed N represents the maximum number of migration generations. re N is the maximum number of replicating generations; c For maximum tendency algebra; N s P is the maximum stride length during swimming. ed For migration probability;

[0056] Step 2: Randomly initialize the bacterial community locations and calculate the initial fitness function value J for each bacterium;

[0057] Step 3: Migration cycle l = l + 1;

[0058] Step 4: Copy the loop k = k + 1;

[0059] Step 5: Approach the loop j = j + 1.

[0060] (1) Calculate the bacterial fitness function J(i,j,k,l), and denote the current optimal fitness value J of bacterial i. last = J(i,j,k,l);

[0061] (2) Rotation: Generate a random vector Δ(i)∈R n Each element Δx(i), x=1,2,3,…,n is a random number distributed on [-1,1].

[0062] (3) Swimming: When m < Ns, m represents the swimming step size.

[0063] (a) If the bacterial fitness value J(i, j, k, l) < Jlast, then update the bacterial position function using Equation (13) to enable the bacteria to continue swimming at the new position.

[0064] (b) If the bacterial fitness value J(i, j, k, l) = Jlastt, then the optimal bacterial fitness value remains unchanged.

[0065] (c) If the bacterial fitness value J(i, j, k, l) > Jlastt, then save the current bacterial fitness value to Jlast until n = N s At this time, calculate the next bacterium i + 1;

[0066] Step 6: If j < Nd, return to Step 5;

[0067] Step 7: Calculate the fitness value of each bacterium i after Nd chemotaxes , and sort the obtained bacterial fitness values in ascending order, eliminate the bacteria with smaller fitness values, and replicate the bacteria with larger fitness values. Each bacterium divides into two identical bacteria;

[0068] Step 8: If k < N re , then return to Step 4;

[0069] Step 9: Each bacterium is randomly distributed into the optimization space according to the migration probability P ed If l < N ed , then return to Step 3, otherwise the optimization ends.

[0070] In a second aspect, an active distribution network grid system considering distributed photovoltaic and electric vehicles provided by an embodiment of the present application includes: a memory and a processor. The memory includes a program for an active distribution network grid method considering distributed photovoltaic and electric vehicles. When the program for the active distribution network grid method considering distributed photovoltaic and electric vehicles is executed by the processor, the above steps are implemented.

[0071] In a third aspect, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores program codes. When the program codes are executed by a processor, the steps of the above-mentioned active distribution network grid method considering distributed photovoltaic and electric vehicles are implemented.

[0072] Compared with existing technologies, the beneficial effects of this invention are as follows: Addressing the power system peak-shaving problem caused by the grid connection of large-scale renewable energy sources such as wind power and photovoltaics, this invention proposes a multi-energy system complementary coordinated optimization scheduling method that considers peak-shaving initiative and demand response. Price-based demand response can guide the demand side to actively participate in load adjustment. Combining the deep peak-shaving characteristics of thermal power units, and utilizing an improved simulated annealing algorithm, this invention proposes a multi-energy system optimization scheduling strategy that considers peak-shaving initiative and demand response. Taking into account the interests of all participants and their initiative in participating in peak-shaving, this model can adjust the distribution of benefits between the affected and benefited parties, adjusting the output of thermal power units under the premise of benefit, maximizing unit flexibility, effectively promoting renewable energy consumption, improving the overall peak-shaving efficiency of the system, and providing an effective method for multi-energy system coordinated scheduling decisions. Attached Figure Description

[0073] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0075] Figure 2 This is a preliminary mesh division diagram of the nodes in this embodiment of the present disclosure;

[0076] Figure 3 This is a real-time power output curve of distributed photovoltaic and electric vehicle in an embodiment of this disclosure;

[0077] Figure 4 This is the optimized mesh partitioning diagram in this embodiment of the disclosure;

[0078] Figure 5 This is a comparison chart of the average absorption rate of the 94-node network in the embodiments of this disclosure;

[0079] Figure 6 This is a comparison chart of the average absorption rate of grid 1 in the embodiments of this disclosure;

[0080] Figure 7 This is a comparison chart of the average absorption rate of grid 2 in the embodiments of this disclosure;

[0081] Figure 8 This is a comparison chart of the average absorption rate of grid 3 in the embodiments of this disclosure;

[0082] Figure 9 This is a comparison diagram of line loss before and after reconstruction and optimization in the embodiments of this disclosure. Detailed Implementation

[0083] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0084] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0086] This disclosure provides an active distribution network gridding method that considers distributed photovoltaic and electric vehicles, including:

[0087] S1: Model the uncertainty and construct the uncertainty probability model for distributed photovoltaics and electric vehicles;

[0088] S2: With the goal of maximizing the distributed photovoltaic absorption rate and minimizing line loss, the particle swarm search formula is improved by using the bacterial foraging algorithm to achieve network reconstruction optimization and re-divide the grid.

[0089] S3: Validation in an IEEE 94-node system that includes distributed photovoltaics and electric vehicles.

[0090] In S1, the Wasserstein probability index model is first adopted as the uncertain output model for distributed photovoltaic power. This model aims to transform continuous probability density functions into discrete values ​​with probabilistic characteristics: first, the probability density functions of photovoltaic and wind power are used to generate output values ​​with probabilistic characteristics at a certain moment through the Wasserstein probability index; second, a set of scenarios containing more moments is generated by connecting them through Cartesian products.

[0091] The formula for the quantiles of the Wasserstein probability index is:

[0092] (1)

[0093] In the formula: g(x) is the probability density function of the random variable; r is the order; c is the c-th quantile; C is the number of optimal quantiles.

[0094] The long-term statistical regularity of the randomness and uncertainty of solar irradiance conforms to a Beta function, and solar power output is linearly related to solar irradiance; therefore, solar power output approximately follows a Beta distribution.

[0095] (2)

[0096] In the formula: P pv P represents output power. max This represents the extreme value of the output power; α is the gamma function; β are the shape parameters of the Beta function.

[0097] Solving using formula (1), we get:

[0098] (3)

[0099] Secondly, in the electric vehicle output model, the charging power is a continuous probability density function of the charging start time and the charging duration.

[0100] The data patterns of charging duration and start time approximate a normal distribution. Using the Wasserstein probability index formula (1), we can obtain...

[0101] (4)

[0102] μ represents the mean of the distribution, and σ represents the standard deviation.

[0103] In S2, to achieve flexible and efficient operation of the active distribution network grid, considering power flow constraints such as power, voltage, and current, as well as distributed generation power constraints, a comprehensive optimization is performed with the goal of maximizing the distributed generation absorption rate and minimizing line losses, and a reconfiguration model is established.

[0104] 1. Distributed Generation Utilization Rate: This indicator is defined as the ratio of the actual output of distributed generation sources to the real-time load of the distribution network. The average distributed generation utilization rate refers to the average utilization level of all distributed generation sources within the distribution network, which better reflects the supply capacity of distributed generation sources. A higher average distributed generation utilization rate is better. The expression is:

[0105] (5)

[0106] (6)

[0107] λ pvi P represents the grid connection utilization rate (%) of the i-th distributed photovoltaic system. pviLet be the active power absorbed by the i-th distributed photovoltaic system (kW). Let be the installed capacity (kW) of the i-th distributed photovoltaic system. denoted as the average grid absorption rate (%); n represents the total number of distributed photovoltaic (PV) systems in the grid.

[0108] 2. Line Losses: Line losses are an important indicator reflecting the economic efficiency of a power grid. While ensuring the safe operation of the system, line losses should be minimized as much as possible. The expression is:

[0109] (7)

[0110] Total power grid line loss (kW); I i R is the current in branch i (A); i Let n be the resistance (Ω) of branch i; n1 be the number of branches in the power grid; S b The state of switch b in branch is 1 when closed and 0 when open.

[0111] 3. Constraints:

[0112] (1) Power balance constraint:

[0113] (8)

[0114] Among them, P i P represents the active power of the i-th node. pvi P represents the active power of the distributed power source at the i-th node; Li V represents the active power of the load at the i-th node; i V j Let Y be the voltage at nodes i and j; Y is the branch admittance matrix.

[0115] (2) Voltage constraint:

[0116] (9)

[0117] in, , Let be the lower and upper limits of the allowable voltage for the i-th node.

[0118] (3) Branch current constraints:

[0119] (10)

[0120] in, , These are the lower and upper limits of the current in branch b.

[0121] (4) Active power output constraints of distributed generation:

[0122] (11)

[0123] in, , Let be the lower and upper limits of the active power output of the distributed photovoltaic system at the i-th node.

[0124] (5) Constraint on the number of switching actions:

[0125] (12)

[0126] Where N is the total number of switches; , Switch i: Number of actions per day, maximum allowed number of actions; This represents the total number of allowed actions of the switch.

[0127] 4. Dynamic optimization and reconstruction:

[0128] This disclosure improves the particle swarm optimization algorithm by employing a bacterial foraging algorithm. The bacterial foraging algorithm mainly includes three steps: attraction, replication, and migration. Let represent the position of the i-th bacterium after the j-th directional operation, the k-th reproductive operation, and the l-th migration operation. Δ(i) is a unit vector in a random direction, and C(i) is the swimming step size of the i-th bacterium. By improving the swimming step size C(i) of the bacteria, the algorithm's computational accuracy is improved while ensuring its convergence speed, allowing the algorithm to escape local minima, as shown in equations (13)-(15).

[0129] (13)

[0130] (14)

[0131] (15)

[0132] (16)

[0133] Where C max and C min These represent the maximum and minimum step sizes, respectively, where t is the current iteration number. max This represents the maximum number of iterations. frw ij x represents the coordinates of the position at a random step size. ij v represents the current position of particle i. ij It is the current flight speed of particle i. The inertia weight coefficient; t represents the iteration number; p i ,j (t) represents the best historical position that particle i has experienced so far; p g,jThis is the best position found by the current particle swarm search; c1 and c2 are learning factors, referred to as cognitive learning factors and social learning factors, respectively. The values ​​are between 0 and 2. r0, r1, and r2 are three independent random numbers in [0, 1]. ||*|| is the Euclidean norm to avoid excessive position updates due to excessively large vector lengths.

[0134] The main steps of the algorithm are as follows:

[0135] Step 1: Initialize parameters M and N ed N re N d N s , P ed Where M is the bacterial population size; N ed N represents the maximum number of migration generations. re N is the maximum number of replicating generations; c For maximum tendency algebra; N s P is the maximum stride length during swimming. ed For migration probability;

[0136] Step 2: Randomly initialize the bacterial community locations and calculate the initial fitness function value J for each bacterium;

[0137] Step 3: Migration cycle l = l + 1;

[0138] Step 4: Copy the loop k = k + 1;

[0139] Step 5: Approach the loop j = j + 1.

[0140] (1) Calculate the bacterial fitness function J(i,j,k,l), and denote the current optimal fitness value J of bacterial i. last = J(i,j,k,l);

[0141] (2) Rotation: Generate a random vector Δ(i)∈R n , where each element Δ x (i), (x=1,2,3,…,n) are random numbers distributed on [-1,1];

[0142] (3) Swimming: When m <N s (where m represents the step length of the swim)

[0143] (a) If the bacterial fitness value J(i,j,k,l) <J last Then, the bacterial position function is updated using equation (13), so that the bacteria can continue to swim in the new position;

[0144] (b) If the bacterial fitness value J(i,j,k,l) ​​= J last The optimal fitness value of the bacteria remains unchanged.

[0145] (c) If the bacterial fitness value J(i,j,k,l) ​​> J last Then save the current bacterial fitness value to J. last Until n=N s When the time comes, calculate the next bacterium i+1;

[0146] Step 6: If j <N d Return to step 5;

[0147] Step 7: Calculate N for each bacterium i d Fitness value after secondary tendency The obtained bacterial fitness values ​​are sorted in ascending order, bacteria with lower fitness values ​​are eliminated, and bacteria with higher fitness values ​​are replicated, with each bacterium splitting into two identical bacteria.

[0148] Step 8: If k <N re Then return to step 4;

[0149] Step 9: Each bacterium is classified according to its migration probability P. ed They are randomly distributed into the optimization space. If l <N ed If the result is positive, return to step 3; otherwise, the optimization process ends.

[0150] In S3, taking the IEEE 94-node system as an example, this power distribution system includes 96 branches, 94 nodes, and 13 tie switches, with a rated voltage of 11.4kV. Based on the grid partitioning principle described above, this 94-node network can be divided into 3 management units. The access nodes for distributed photovoltaic and electric vehicles in the system and their capacities are shown in the table below:

[0151] Table 1. Distributed Photovoltaic and Electric Vehicle Access Nodes and Capacity Table

[0152]

[0153] Assuming that under the same regional and temporal conditions, the power output patterns of similar types of distributed photovoltaic (PV) power and electric vehicles remain consistent, the daily power output variations of PV and electric vehicles... Figure 3 :

[0154] After the reconstruction and optimization in Part 3, and the disconnection of the corresponding branches, the changes in the three mesh structures are shown in the following figure: (and) Figure 2 compared to, Figure 4The main change is that branch D, which originally belonged to grid 3, has been moved to grid 2. Grid 1 did not adjust its grid boundaries, but only performed local optimization, disconnecting branches (6-7) and (52-53) and adding connecting branches (5-55) and (7-60); Grid 2 adjusted its grid structure, disconnecting branch (12-14) and adding connecting branches (14-18) and (16-26); Grid 3 also adjusted its grid structure, removing branch D.

[0155] A comparative analysis was conducted on the active distribution network reconfiguration optimization before and after: Figure 5-8 The chart comparing the average absorption rate of distributed generation clearly shows that the average absorption rate has significantly improved and is more stable, indicating that the distribution network has stronger anti-interference capabilities. Figure 9 This is a comparison chart of losses before and after line reconfiguration, by... Figure 9 It can be seen that the line loss has decreased significantly, especially around 15:00, when the decrease is most pronounced. Figure 5 It can be seen that after dynamic reconfiguration and optimization, the average distributed generation absorption rate of the active distribution network significantly increased by 17 percentage points; the distributed generation absorption rate of each sub-grid also significantly improved, with grid 1 increasing by 16.54%, grid 2 by 18.4%, and grid 3 by 17.09%. This demonstrates that this method can improve the utilization rate of renewable energy and solve the problem of wind and solar curtailment in some areas. Figure 9 It can be seen that after dynamic reconfiguration and optimization, the losses of the active distribution network are significantly reduced, with a noticeable reduction in losses between 10:00 and 17:00, especially with a 50% reduction in line losses at 15:00. This indicates that grid-based partitioning can effectively reduce losses and achieve economical and efficient operation of the active distribution network.

[0156] This application provides an active distribution grid meshing system that considers distributed photovoltaic and electric vehicles. The system includes a memory and a processor. The memory includes a program for an active distribution grid meshing method that considers distributed photovoltaic and electric vehicles. When the program for an active distribution grid meshing method that considers distributed photovoltaic and electric vehicles is executed by the processor, it implements the steps described above.

[0157] This application provides a computer-readable storage medium storing program code. When the program code is executed by a processor, it implements the steps of the active distribution grid meshing method considering distributed photovoltaics and electric vehicles as described above.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0163] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0164] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0165] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0166] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An active distribution network gridding method considering distributed photovoltaic and electric vehicles, characterized in that, It includes the following specific steps: S1: Model the uncertainty and construct the uncertainty probability models of distributed photovoltaic and electric vehicles; S2: With the goal of maximizing the absorption rate of distributed photovoltaic and minimizing the line loss, adopt the bacterial foraging algorithm to improve the particle swarm search formula, realize the optimization of network reconfiguration and re-divide the grid; S3: Verify in the IEEE 94-node system including distributed photovoltaic and electric vehicles.

2. The active distribution network gridding method considering distributed photovoltaic and electric vehicles according to claim 1, characterized in that, The modeling of the uncertainty and constructing the uncertainty probability models of distributed photovoltaic and electric vehicles is specifically as follows: Adopt the Wasserstein probability index model as the uncertainty output model of distributed photovoltaic, convert the continuous probability density function into discrete values with probability characteristics, generate the output value with probability characteristics at a certain moment through the Wasserstein probability index for the probability density functions of photovoltaic and wind power; generate a scenario set containing more moments through the Cartesian product connection.

3. The active distribution network gridding method considering distributed photovoltaic and electric vehicles according to claim 2, characterized in that, The quantile formula of the Wasserstein probability index is: （1） In the formula: g(x) is the probability density function of the random variable; r is the order; c is the c-th quantile; C is the number of optimal quantiles. The long-term statistical law of the randomness and uncertainty of light intensity conforms to the Beta function, and the photovoltaic output is linearly related to the light intensity. Therefore, the photovoltaic output approximately follows the Beta distribution: （2） In the formula: Ppv is the output power; Pmax is the extreme value of the output power; Let be the gamma function; α and β are the shape parameters of the Beta function. Solve by combining formula (1): （3） Secondly, in the electric vehicle output model, the charging power is the continuous probability density function of the charging start time and charging duration. The data law of the charging duration and start time approximately follows the normal distribution. Solve by combining the Wasserstein probability index formula (1). （4） μ represents the mean of the distribution, and σ represents the standard deviation.

4. The active distribution network gridding method considering distributed photovoltaic and electric vehicles according to claim 3, characterized in that, The specific implementation of taking the maximization of the absorption rate of distributed photovoltaic as the goal, taking the minimization of line loss as the goal, adopting the bacterial foraging algorithm to improve the particle swarm search formula, realizing the optimization of network reconfiguration and re-dividing the grid is as follows: Absorption rate of distributed power sources: This index is defined as the ratio of the actual output of distributed power sources to the real-time load of the distribution network. The average absorption rate of distributed power sources refers to the average absorption level of all distributed power sources in the distribution network, which can better reflect the supply capacity of distributed power sources. The larger the defined average absorption rate index of distributed power sources, the better. The expression is: （5） （6） λ pvi P represents the grid connection efficiency of the i-th distributed photovoltaic system. pvi Let be the active power absorbed by the i-th distributed photovoltaic system; Let be the installed capacity of the i-th distributed photovoltaic system; is the average grid absorption rate; n is the total number of distributed photovoltaic (PV) systems in the grid. Line loss: Line loss is an important index reflecting the economy of the power grid. On the basis of ensuring the safe operation of the system, the line loss should be reduced as much as possible. The expression is: （7） Total power grid line losses; I i Let be the current in branch i; Ri be the resistance in branch i; n1 be the number of branches in the power grid; S be the current in branch i; b For branch circuit b, the switch status is 1 when closed and 0 when open. Constraint conditions: (1) Power balance constraint: （8） Among them, P i P represents the active power of the i-th node. pvi P represents the active power of the distributed power source at the i-th node; Li V represents the active power of the load at the i-th node; i V j Let Y be the voltage at nodes i and j; Y is the branch admittance matrix. Voltage constraint: （9） in, , Let be the lower and upper limits of the allowable voltage for the i-th node. [[ID=Y19]]Branch current constraint: （10） in, , Let these be the lower and upper limits of the current in branch b. Active power output constraint of distributed power sources: （11） in, , Let these be the lower and upper limits of the allowable active power output of the distributed photovoltaic system at the i-th node. Switch operation times constraint: （12） Where N is the total number of switches; , Switch i: Number of actions per day, maximum allowed number of actions; This represents the total number of allowed actions of the switch. Dynamic optimization reconfiguration: The particle swarm optimization algorithm is improved by employing a bacterial foraging algorithm. The bacterial foraging algorithm mainly includes three steps: attraction, replication, and migration. Let represent the position of the i-th bacterium after the j-th directional operation, the k-th reproductive operation, and the l-th migration operation, Δ(i) be a unit vector in a random direction, and C(i) be the swimming step size of the i-th bacterium. By improving the swimming step size C(i) of the bacteria, the algorithm's computational accuracy is improved while ensuring the algorithm's convergence speed, allowing the algorithm to escape local minima, as shown in equations (13)-(15): （13） (14) (15) （16) Where C max and C min These represent the maximum and minimum step sizes, respectively, where t is the current iteration number. max f is the maximum number of iterations. rwij Let x be the coordinates of the random step size position. ij v represents the current position of particle i. ij It is the current flight speed of particle i. The inertia weight coefficient; t represents the iteration number; p ij p represents the best historical position that particle i has experienced so far; gj It is the best position found by the current particle swarm search; c1 and c2 are learning factors, called cognitive learning factors and social learning factors, respectively, taking values ​​between 0 and 2; r0, r1, and r2 are three independent random numbers on [0, 1]; ||*|| is the Euclidean norm to avoid excessive position update due to excessive vector length.

5. The active distribution network gridding method considering distributed photovoltaic and electric vehicles according to claim 4, characterized in that, The specific implementation of the bacterial foraging algorithm to improve the particle swarm optimization algorithm is as follows: Step 1: Initialize parameters M and N ed N re N d N s , P ed Where M is the bacterial population size; N ed N represents the maximum number of migration generations. re N is the maximum number of replicating generations; c For maximum tendency algebra; N s P is the maximum stride length during swimming. ed For migration probability; Step 2: Randomly initialize the positions of the bacterial colony and calculate the initial fitness function value J of each bacterium; Step 3: Migration loop l = l + 1; Step 4: Replication loop k = k + 1; Step 5: Taxis loop j = j + 1. (1) Calculate the bacterial fitness function J(i,j,k,l), and denote the current optimal fitness value J of bacterial i. last = J(i,j,k,l); (2) Rotation: Generate a random vector Δ(i)∈R n Each element Δx(i), x=1,2,3,…,n is a random number distributed on [-1,1]. (3) Swarming: When m < Ns, m represents the swarming step size. (a) If the bacterial fitness value J(i,j,k,l) < Jlast, the bacterial position function is updated using Equation (13) so that the bacteria continue to swim at the new position; (b) If the bacterial fitness value J(i,j,k,l) = Jlastt, the optimal bacterial fitness value remains unchanged; (c) If the bacterial fitness value J(i,j,k,l)>Jlastt, then save the current bacterial fitness value to Jlastt until n=N. s When the time comes, calculate the next bacterium i+1; Step 6: If j < Nd, return to Step 5; Step 7: Calculate the fitness value of each bacterium i after Nd homing cycles. The obtained bacterial fitness values ​​are sorted in ascending order, bacteria with lower fitness values ​​are eliminated, and bacteria with higher fitness values ​​are replicated, with each bacterium splitting into two identical bacteria. Step 8: If k <N re Then return to step 4; Step 9: Each bacterium is classified according to its migration probability P. ed They are randomly distributed into the optimization space, if l <N ed If the result is positive, return to step 3; otherwise, the optimization process ends.

6. An active distribution grid system considering distributed photovoltaic and electric vehicles, characterized in that, The system includes: a memory and a processor. The memory includes a program for an active distribution network grid method considering distributed photovoltaic and electric vehicles. When the program for the active distribution network grid method considering distributed photovoltaic and electric vehicles is executed by the processor, the steps described above are implemented.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code. When the program code is executed by the processor, the steps of the active distribution network grid method considering distributed photovoltaic and electric vehicles according to any one of claims 1 to 5 are implemented.

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