Power supply optimization control method of distribution network based on multi-DER access

Abstract

The present invention belongs to the field of power supply optimization control of distribution networks, and specifically is a power supply optimization control method for distribution networks based on multi-DER access. The present invention achieves the minimization of power supply costs and the maximization of power supply reliability through in-depth analysis of load characteristics and geographical distribution, combined with the scheduling strategy of DER. Through non-dominated sorting and congestion calculation, the optimal chromosome is selected to enter the next generation, ensuring the diversity and optimization effect of power supply schemes. Ultimately, the method can provide an efficient, economical and reliable power supply optimization control scheme for distribution networks, and has broad application prospects and practical value.

Description

Technical Field

[0001] The present invention belongs to the field of power supply optimization control of distribution network, and specifically to a power supply optimization control method of distribution network based on multi-DER access. Background Art

[0002] With the rapid development of renewable energy technology, distributed energy resources (DER) such as solar photovoltaic and wind power are increasingly being used in distribution networks. The access of these resources provides new options and flexibility for the power supply of distribution networks, but also brings new challenges. Traditional distribution network power supply optimization control methods mainly rely on centralized power generation and scheduling strategies, which makes it difficult to fully utilize the potential and advantages of DER.

[0003] First, the intermittent and unpredictable nature of DERs causes their output power to fluctuate greatly, which places higher demands on the stability and reliability of the distribution network. Second, DERs are widely and dispersedly distributed geographically, and how to effectively manage and dispatch these resources to meet the load demands of different regions has become an urgent problem to be solved. In addition, with the development of the power market, users have higher and higher requirements for power supply quality. How to reduce power supply costs and improve economic benefits while ensuring power supply reliability is also an important issue facing the current optimization and control of distribution networks.

[0004] Existing power supply optimization control methods mainly focus on load forecasting and the scheduling of traditional power generation resources, and lack in-depth consideration of DER characteristics. For example, some methods adjust power generation plans and grid operation modes through load forecasting, but these forecasts often have errors and are difficult to respond to changes in DER output in real time. In addition, some methods adjust the operating parameters of the grid through optimization algorithms, but these algorithms usually only consider local optimization and do not consider the power supply capacity and economic benefits of the distribution network as a whole.

[0005] In response to the above problems, although some studies have attempted to incorporate DER into power supply optimization control, these studies are mostly limited to a single DER type or a small-scale distribution network, lacking comprehensive consideration of multiple types of DER and large-scale distribution networks. In addition, these studies also face challenges in practical applications, such as high algorithm complexity, long calculation time, and difficulty in achieving real-time optimization. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention proposes a distribution network power supply optimization control method based on multi-DER access. By converting the power supply partitioning and DER scheduling strategy of the distribution network into chromosome coding, and applying selection, crossover and mutation for optimization, the power supply cost is minimized and the power supply reliability is maximized, thereby ensuring the stable operation and economy of the power grid.

[0007] The technical solution of the present invention is as follows:

[0008] The power supply optimization control method of the distribution network based on multi-DER access includes:

[0009] Collect the load characteristic data of the distribution network in the power supply area, the geographical distribution coordinates of the power supply area, the distribution coordinates of DERs and the output data of DERs;

[0010] Divide the power supply zones according to the load characteristic data, formulate the DER dispatching strategy according to the number of DERs, convert the power supply zones and DER dispatching strategy into chromosome codes, randomly generate a certain number of chromosomes, and form an initial population;

[0011] Generate a fitness function based on cost minimization, calculate the fitness of each chromosome in the initial population according to the fitness function, perform non-dominated sorting and crowding calculation based on the fitness, and select chromosomes with higher fitness to enter the next generation;

[0012] Perform crossover and mutation operations on the selected chromosomes to generate new chromosomes, evaluate the fitness of the newly generated chromosomes, repeat the selection, crossover and mutation operations until the fitness reaches the maximum number of iterations, and finally output the chromosome with the highest fitness, that is, the optimal power supply optimization control solution; decode the chromosome of the optimal solution and restore it to the actual power supply partition and DER scheduling strategy.

[0013] Furthermore, the power supply partitions are divided as follows:

[0014] Select a time period, accumulate the load of each node in the selected time period, and obtain the total load;

[0015] According to the load size and geographical location, the total load density and node load density are calculated. By comparing the node load density and the total load density, the high-density load nodes are screened out and the number of power supply partitions is determined. ;

[0016] Determine a center point for each standard load high-density point, and divide the boundaries of each partition according to the requirements of regional load demand averaging.

[0017] Furthermore, the total load density is calculated as follows: ;in, is the total load, is the total area of ​​the power supply area;

[0018] The node load density is calculated as follows: ;in, For the Node load, For the The area of ​​a node.

[0019] Furthermore, the DER dispatching strategy formulation method is: m DERs in the power supply area are combined in different ways to form m! DER dispatching strategy sets.

[0020] Furthermore, the chromosome code is in binary form, and the chromosome consists of two parts, a power supply partition code and a DER scheduling strategy code. The power supply partition code is located at the front of the chromosome, and the DER scheduling strategy code is located at the back of the chromosome.

[0021] Furthermore, the fitness function generated based on cost minimization is:

[0022] ;

[0023] in, is cost-effective, ; is power supply reliability, , for unmet load; is the load satisfaction rate, , is the actual power supply; is the corresponding weight.

[0024] Furthermore, the congestion calculation formula is:

[0025] ;

[0026] in, It is The fitness value of a chromosome, is the average fitness value of the chromosomes in the dominant layer.

[0027] Furthermore, the crossover operation is divided into single-point crossover or multi-point crossover;

[0028] The variation operation is: changing the coding order of the power supply partitions or changing the opening and closing of one or more DERs.

[0029] The beneficial effects of the present invention are as follows: the present invention provides a distribution network power supply optimization control method based on multi-DER access, which realizes efficient division of power supply areas and optimization of DER scheduling strategies by integrating load characteristic data, geographical distribution and DER resources. The method adopts a genetic algorithm to convert power supply partitions and DER scheduling strategies into chromosome codes, and iterates and optimizes through fitness evaluation, selection, crossover and mutation operations to finally obtain an optimal control scheme with maximum cost-effectiveness and high power supply reliability. Its advantages are that it can significantly improve the operating efficiency of the distribution network, reduce the power supply cost, and enhance the adaptability and stability of the power grid, providing an innovative solution for the intelligent management of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The overall flow chart of the present invention is shown.

[0031] Figure 2 A specific implementation of S100 data collection is shown.

[0032] Figure 3 The specific implementation methods of S200 power supply zoning and DER scheduling strategy formulation are demonstrated.

[0033] Figure 4 The specific implementation of S300 fitness evaluation and selection is demonstrated.

[0034] Figure 5 The specific implementation of S400 crossover, mutation and optimization is demonstrated. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] See also Figure 1 , which shows a distribution network power supply optimization control method based on multi-DER access provided by an embodiment of the present invention, including:

[0037] S100. Collect load characteristic data of the distribution network in the power supply area, the geographical distribution coordinates of the power supply area, the distribution coordinates of distributed energy resources (DER) and the output data of DER;

[0038] S200. Divide the power supply partitions according to the load characteristic data, form a DER dispatching strategy according to the number of DERs, convert the power supply partitions and the DER dispatching strategy into chromosome codes, randomly generate a certain number of chromosomes, and form an initial population.

[0039] S300. Generate a fitness function based on cost minimization, and calculate the fitness of each chromosome according to the fitness function; perform non-dominated sorting and crowding calculation based on the fitness, and select chromosomes with higher fitness to enter the next generation.

[0040] S400. Perform crossover and mutation operations on the selected chromosomes to generate new chromosomes, evaluate the fitness of the newly generated chromosomes, repeat the selection, crossover and mutation operations until the fitness reaches the maximum number of iterations, and finally output the chromosome with the highest fitness, that is, the optimal power supply optimization control solution; decode the chromosome of the optimal solution and restore it to the actual power supply partition and DER scheduling strategy.

[0041] S100 involves collecting and integrating distribution network load characteristic data, geographical distribution coordinates, distribution coordinates of distributed energy resources (DERs) and their output data within the power supply area.

[0042] In some possible implementations, such as Figure 2 As shown, S100 specifically includes:

[0043] S110. Collect power load data within the power supply area, including but not limited to load size, load change rate, etc.

[0044] S120. Determine the geographic coordinates of each important node in the power supply area, including the coordinates of key nodes such as each load point, substation, and distribution line. Geographic coordinates are usually expressed in longitude and latitude, which can be obtained through GPS equipment or geographic information system (GIS).

[0045] S130. Collect the geographic location and output power data of distributed energy resources (such as solar photovoltaic, wind power, etc.). The output power of DER can be obtained through its actual operation data.

[0046] S100 provides detailed data support for power supply optimization control by comprehensively considering the load characteristics, geographical distribution, and distribution and output of distributed energy resources of the distribution network.

[0047] S200 converts the power supply partitioning and DER dispatching strategy of the distribution network into chromosome coding, and randomly generates an initial population to provide an initial solution set for the subsequent one.

[0048] In some possible implementations, such as Figure 3 As shown, S200 specifically includes:

[0049] S210. Division of power supply zones.

[0050] The power supply zones are divided as follows:

[0051] S211. Select one day or one week to perform load calculation, and add up the load of each node in the selected time period to obtain the total load.

[0052] S212. Calculate the total load density and node load density based on the load size and geographical location, screen out high-density load nodes by comparing the node load density and the total load density, and determine the number of power supply partitions .

[0053] The screening method for high-density nodes is:

[0054] Calculate the total load density: ;in, is the total load, is the total area of ​​the power supply area;

[0055] Calculate node load density: ;in, For the Node load, For the The area of ​​each node;

[0056] The load nodes whose node load density is greater than the total load density are regarded as high-density load nodes, and the number of power supply partitions is counted. .

[0057] S213. Determine a center point for each standard load high-density point, and divide the boundaries of each partition according to the requirements of regional load demand averaging.

[0058] Assume that a power supply area contains 4 nodes, and the node information is shown in Table 1:

[0059] Table 1 Node information

[0060]

[0061] A week is selected as the time range for load calculation. The total load density calculated is 4.2MW / km², and the load densities of each node are 5MW / km², 4.67MW / km², 6MW / km², and 3MW / km² respectively. The load density of nodes 1, 2, and 3 is greater than the threshold, so they are high-density load nodes, so three power supply partitions are set. Based on the requirements of regional load demand averaging and the geographical location of the center point, GIS tools or distance-based methods are used to determine the partition boundaries.

[0062] S220. DER dispatch strategy formulation: m DERs in the power supply area are combined to form m! combined DER dispatch strategies.

[0063] DER dispatch strategy refers to how to manage and dispatch each DER in the power supply area to meet the load demand. Different combinations of m DERs in the power supply area form a comprehensive dispatch strategy set. In theory, the number of all possible combinations of m DERs is m! .

[0064] S230. Convert the power supply partition and DER dispatch strategy into chromosome codes, where each chromosome represents a possible power supply optimization control scheme. The chromosome code is in binary form.

[0065] The chromosome consists of two parts: power supply partition code and DER scheduling strategy code. The power supply partition code is located at the front of the chromosome, and the DER scheduling strategy code is located at the back of the chromosome.

[0066] The power supply partition code is:

[0067] The load characteristics of power supply zone 1 are represented by binary 101;

[0068] The load characteristics of power supply zone 2 are represented by binary 011;

[0069] The load characteristics of power supply zone 3 are represented by binary 110;

[0070] The DER scheduling strategy is encoded as:

[0071] DER1 information is represented by binary 101, open (1), closed (0);

[0072] DER2 information is represented by binary 011, open (1), closed (0);

[0073] DER3 information is represented by binary 110, open (1), closed (0);

[0074] DER4 information is represented by binary 110, open (1), closed (0);

[0075] Assume that the power supply area is divided into three power supply zones, each zone is represented by a binary number to represent its load characteristics. At the same time, there are four DERs in the power supply area, each DER has two states (on or off).

[0076] Power supply partition code:

[0077] The load characteristics of power supply zone 1 are represented by binary 101;

[0078] The load characteristics of power supply zone 2 are represented by binary 011;

[0079] The load characteristics of the power supply section 3 are represented by binary 110 .

[0080] DER scheduling strategy encoding:

[0081] DER1--101: open (1);

[0082] DER2--011: Open (1);

[0083] DER3--110: Close (0);

[0084] DER4--110: On (1).

[0085] Chromosome encoding generation:

[0086] Chromosome code = (101, 011, 110, 1011, 0111, 1100, 1101);

[0087] S240. Randomly generate a certain number of chromosomes to form an initial population.

[0088] Based on the chromosome encoding of S230, a sufficient number of chromosomes are generated to form a diverse initial population, which provides a basis for subsequent fitness evaluation, selection, crossover and mutation operations.

[0089] S300 aims to construct a fitness function based on cost minimization. By calculating the comprehensive performance of the power supply optimization control scheme represented by each chromosome in terms of cost-effectiveness, power supply reliability and load satisfaction rate, it uses non-dominated sorting and congestion evaluation methods to select the optimal chromosome to enter the next generation population, and then iteratively optimizes the power supply partitioning and DER scheduling strategy, and finally realizes the power supply optimization control of the distribution network.

[0090] In some possible embodiments, Figure 4 As shown, S300 specifically includes:

[0091] S310. Determine the objectives and components of the fitness function, including cost-effectiveness, power supply reliability, load satisfaction rate, etc. The fitness function:

[0092] ;

[0093] in, is cost-effective, ; is power supply reliability, , for unmet load; is the load satisfaction rate, , is the actual power supply; is the corresponding weight.

[0094] S320. Perform non-dominated sorting based on fitness values ​​and divide chromosomes into different dominant layers. The first layer is the Pareto optimal solution set, that is, the solution that is not the worst in all the considered indicators. The other layers are dominated solution sets.

[0095] S330. For each chromosome in the dominance layer, calculate its crowding degree, which reflects the distribution density of the chromosome in the target space.

[0096] The formula for calculating congestion is:

[0097] ;

[0098] in, It is The fitness value of a chromosome, is the average fitness value of the chromosomes in the dominant layer.

[0099] S340. Based on non-dominated sorting and crowding, chromosomes with higher fitness are selected to enter the next generation.

[0100] The selection method is: give priority to chromosomes in the first layer (Pareto optimal layer). In the same dominant layer, chromosomes with higher crowding are selected to maintain the diversity of the population.

[0101] S350. Organize the selected chromosomes into a new generation population to provide a basis for subsequent crossover and mutation operations.

[0102] Assuming that there are 3 power supply partitions and 4 DERs in the power supply area, each DER has two states (on or off), three sets of chromosomes are initially generated:

[0103] Chromosome 1: ;

[0104] Chromosome 2: ;

[0105] Chromosome 3: ;

[0106] Non-dominated sorting:

[0107] The first layer (Pareto optimal layer): chromosome 3;

[0108] The second layer: chromosome 1 and chromosome 2;

[0109] Calculate the congestion:

[0110] Chromosome 1: Assuming that in the target space, the fitness values ​​of chromosome 1's neighbor chromosomes are 0.67 and 0.68 respectively, then ;

[0111] Chromosome 2: Assuming that in the target space, the fitness values ​​of chromosome 2's neighbor chromosomes are 0.64 and 0.66 respectively, then .

[0112] Chromosome 3: Since it is the Pareto optimal layer, there is no neighbor chromosome, and the crowding degree can be considered to be 0 or the minimum value, then .

[0113] First, we select chromosome 3 in the Pareto optimal layer because it is not the worst in all the indicators considered. In the second layer, we select chromosome 1 with higher crowding because it is more densely distributed in the target space and may represent a better solution.

[0114] S400 applies crossover, mutation, and selection operations to optimize the power supply control scheme. Specifically, it first selects chromosomes from the current population based on fitness and crowding to crossover, generates new chromosomes, and then performs mutation operations on these new chromosomes to increase the genetic diversity of the population. Then, the fitness of the newly generated chromosomes is evaluated, and the best chromosome is selected from them to enter the next generation based on non-dominated sorting and crowding. These steps are repeated until the predetermined fitness threshold or number of iterations is reached, and finally the chromosome with the highest fitness is output as the optimal power supply optimization control scheme, which is decoded into the actual power supply partition and DER scheduling strategy.

[0115] In some possible embodiments, Figure 5 As shown, S400 specifically includes:

[0116] S410. Select chromosomes from the current population for crossover operation. Usually, chromosomes with higher fitness and higher crowding are selected, and a pair of chromosomes are randomly selected from the selected chromosomes for crossover. Crossover can be divided into single-point crossover and multi-point crossover. Single-point crossover is to select a crossover point, cut the two parent chromosomes from the point and exchange them. Multi-point crossover is to select multiple crossover points and exchange the corresponding parts of the parent chromosomes.

[0117] Assume that there are four chromosomes as follows, each chromosome represents a power supply optimization control scheme:

[0118] Chromosome 1: 101011110110101110110;

[0119] Chromosome 2: 110011101101101110110;

[0120] Chromosome 3: 101011110110101011110;

[0121] Chromosome 4: 110011101101101011110;

[0122] Select chromosome 1 and chromosome 2 for single-point crossover operation. The new chromosome generated after crossover is:

[0123] Sub-chromosome 1: 110011110110110110110; sub-chromosome 1 inherits the power supply partitioning code (101011110) of chromosome 1 and the DER scheduling strategy code (110110110110) of chromosome 2.

[0124] Sub-chromosome 2: 101011110110101110110; inherits the power supply partition code (110011101) of chromosome 2 and the DER scheduling strategy code (10110110110110) of chromosome 1.

[0125] S420. Perform mutation operation on the new chromosome generated by crossover to increase the diversity of the population. The mutation operation is: changing the coding order of the power supply partitions or changing the opening and closing of one or more DERs.

[0126] For example:

[0127] Original subchromosome 1: 110, 011, 110, 1101, 1011, 0110;

[0128] After mutation, sub-chromosome 1: 011, 110, 110, 1100, 1011, 0111;

[0129] In this way, new chromosomes are generated through selection, crossover and mutation operations.

[0130] S430. Step by step, execute S300 and S410 and S420 in S400 based on the newly generated chromosome until the fitness reaches the maximum number of iterations.

[0131] S440. Output the chromosome with the highest fitness, that is, the optimal power supply optimization control solution; decode the chromosome of the optimal solution and restore it to the actual power supply partition and DER scheduling strategy.

[0132] For example, the final chromosome is 011101110101110111011, that is, the power supply order is power supply partition 2, power supply partition 1, and power supply partition 3. The DER scheduling strategy for this period is: DER1 is turned on, DER2 is turned on, DER3 is turned off, and DER4 is turned on.

[0133] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0134] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0135] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0136] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0137] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A power supply optimization control method for a distribution network based on multi-DER access, characterized in that: include: Collect the load characteristic data of the distribution network in the power supply area, the geographical distribution coordinates of the power supply area, the distribution coordinates of DERs and the output data of DERs; Divide the power supply zones according to the load characteristic data, formulate the DER dispatching strategy according to the number of DERs, convert the power supply zones and DER dispatching strategy into chromosome codes, randomly generate a certain number of chromosomes, and form an initial population; Generate a fitness function based on cost minimization, calculate the fitness of each chromosome in the initial population according to the fitness function, perform non-dominated sorting and crowding calculation based on the fitness, and select chromosomes with higher fitness to enter the next generation; Perform crossover and mutation operations on the selected chromosomes to generate new chromosomes, evaluate the fitness of the newly generated chromosomes, repeat the selection, crossover and mutation operations until the fitness reaches the maximum number of iterations, and finally output the chromosome with the highest fitness, that is, the optimal power supply optimization control solution; decode the chromosome of the optimal solution and restore it to the actual power supply partition and DER scheduling strategy; The power supply partitions are divided as follows: Select a time period, accumulate the load of each node in the selected time period, and obtain the total load; According to the load size and geographical location, the total load density and node load density are calculated. By comparing the node load density with the total load density, the high-density load nodes are screened out and the number of power supply partitions n is determined; Determine a central point for each standard load high-density point, and divide the boundaries of each partition according to the requirements of regional load demand averaging; Wherein, the total load density is calculated as follows: Among them, P 总 is the total load, S 总 is the total area of ​​the power supply area; The node load density is calculated as follows: Among them, P i is the i-th node load, S i is the area of ​​the ith node; The fitness function generated based on cost minimization is: F = ω1·C + ω2·R + ω3·H; Where C is cost-effectiveness, R is the power supply reliability, P 未满足 is the unsatisfied load; H is the load satisfaction rate, P 实 is the actual power supply; ω1, ω2, ω3 are the corresponding weights; The congestion calculation formula is: Among them, F i is the fitness value of the ith chromosome, is the average fitness value of the dominant layer chromosomes.

2. The power supply optimization control method of the distribution network based on multi-DER access according to claim 1 is characterized in that: The DER dispatch strategy formulation method is as follows: m DERs in the power supply area are combined in different ways to form a set of m! DER dispatch strategies.

3. The power supply optimization control method of the distribution network based on multi-DER access according to claim 1 is characterized in that: The chromosome code is in binary form. The chromosome consists of two parts: a power supply partition code and a DER scheduling strategy code. The power supply partition code is located at the front of the chromosome, and the DER scheduling strategy code is located at the back of the chromosome.

4. The power supply optimization control method of the distribution network based on multi-DER access according to claim 1 is characterized in that: The crossover operation is divided into single-point crossover or multi-point crossover; The variation operation is: changing the coding order of the power supply partitions or changing the opening and closing of one or more DERs.

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