Multi-voltage-level collaborative operation optimization method and system considering new energy consumption

Abstract

The invention discloses a multi-voltage-level collaborative operation optimization method and system considering new energy consumption. The method comprises the following steps: clustering data of a distributed power supply and a load demand in a new energy output operation scene; establishing a single-voltage level operation optimization model considering new energy consumption according to a clustering result, and analyzing an interactive influence between different voltage levels and a weight relationship between multi-dimensional economic indexes; and constructing a double-layer model of multi-voltage-level operation optimization generated based on target function constraint, and carrying out iterative solution on an optimization problem of the double-layer model to realize multi-voltage-level collaborative operation optimization. According to the method, the interactive influence between different voltage level operation modes is fully considered, and the weight of the multi-dimensional new energy economic consumption index is considered, so that the optimization of each voltage level operation mode on the premise of ensuring the safe operation of the power grid is effectively realized, and the safety and economy of new energy consumption are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power consumption, and particularly to a multi-voltage level collaborative operation optimization method and system considering new energy consumption. Background Art

[0002] In recent years, with the in-depth promotion of energy transformation, distributed power sources have become an important part of energy structure optimization and green and low-carbon development. However, with the transformation of traditional passive distribution networks to new active distribution networks, safety problems such as insufficient regulation ability, limited reverse power transmission, and excessive voltage deviation have become increasingly prominent. The two-way power flow that appears in the distribution network, especially the reverse power flow generated during low load periods, may exacerbate the complexity of power flow in the power grid and increase the losses of distribution equipment. In addition, if the power grid's consumption capacity is insufficient, a large amount of clean energy may be wasted or abandoned, seriously affecting the utilization rate of new energy. Therefore, the operation optimization of the power grid under the access of a large amount of new energy has increasingly become a prominent key issue.

[0003] Traditional power grid operation optimization ideas are based on static scheduling, aiming to minimize the losses of electric energy during power grid transmission, improve operation efficiency, reduce energy waste, and enhance the economy of operation. However, under the background of the current "dual carbon" goal and the large-scale access of new energy, traditional operation optimization ideas lack means to cope with the volatility and intermittency of new energy, resulting in inflexible power grid scheduling and difficulty in achieving high-proportion new energy consumption. In addition, cross-regional collaborative scheduling can optimize the allocation of power resources, balance the supply and demand differences in each region, and thus improve the consumption rate of new energy through the interconnection of power grids in multiple regions. Therefore, it is necessary to study a multi-voltage level collaborative operation optimization method considering new energy safe and economic consumption. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a multi-voltage level collaborative operation optimization method and system considering new energy consumption. Considering the uncertainty of new energy output, the operation of the power grid is optimized through means such as network reconfiguration to achieve the safe and economic consumption of new energy.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an optimization method for coordinated operation of multiple voltage levels considering new energy consumption, including: clustering data of distributed power sources and load demands in the new energy output operation scenario; establishing an operation optimization model of a single voltage level considering new energy consumption according to the clustering results, and analyzing the interaction effects between different voltage levels and the weight relationship between multi-dimensional economic indicators; constructing a two-layer model for operation optimization of multiple voltage levels generated based on objective function constraints, and iteratively solving the optimization problem of the two-layer model to achieve coordinated operation optimization of multiple voltage levels.

[0008] As a preferred embodiment of the optimization method for coordinated operation of multiple voltage levels considering new energy consumption according to the present invention, wherein: the clustering of data of distributed power sources and load demands in the new energy output operation scenario includes:

[0009] After collecting the numerical values of distributed power sources and load demands at each moment in all scenarios, the DBSCAN algorithm is used to divide the regions with high data density into clusters, that is, the largest set of density-connected data points, and clusters of any shape are discovered in the spatial database of noise, and then the improved K-means algorithm is used to obtain more accurate clustering results.

[0010] As a preferred embodiment of the optimization method for coordinated operation of multiple voltage levels considering new energy consumption according to the present invention, wherein: the establishment of the operation optimization model of a single voltage level considering new energy consumption includes:

[0011] The economic optimal objective function of the 10kV area operation optimization model is:

[0012]

[0013] where p s represents the probability of the occurrence of scenario s, Δt represents the simulation time interval, represents the new energy consumption power of node i at time t in scenario s, represents the loss power of line ij at time t in scenario s, and α represents a constant;

[0014] The economic optimal objective function of the 110kV area operation optimization model is:

[0015]

[0016] where, represents the network loss power of line ij at time t in scenario s.

[0017] As a preferred embodiment of the optimization method for coordinated operation of multiple voltage levels considering new energy consumption according to the present invention, wherein: the safety constraints of the 10kV area operation optimization model include:

[0018]

[0019] V i,min ≤V i,t,s ≤V i,max

[0020] (P ij,t,s ) 2 +(Q ij,t,s ) 2 ≤(S max ) 2

[0021]

[0022] I d ≤I dmax

[0023] where M is a constant large enough, z ij ,s is a 0 / 1 variable representing the open / closed state of line ij, 0 means the line is open, and 1 means the line is connected. V i,t,s represents the voltage of node i, V j,t,s is the voltage of node j, P ij,t,s and Q ij,t,s respectively represent the active power and reactive power flowing through line ij, R ij and X ij respectively represent the resistance and reactance of line ij, I ij,t,s represents the current flowing through line ij, V i,min and V i,max are respectively the minimum voltage and maximum voltage of node i, S max represents the maximum apparent power that the line can pass through, is the total harmonic distortion rate of the voltage of phase s at node k in this distribution network, is the maximum total harmonic distortion rate of the voltage, I d is the short-circuit current of the system, I dmax is the maximum short-circuit current value specified by the national standard.

[0024] As a preferred solution of the multi-voltage-level collaborative operation optimization method considering new energy consumption described in the present invention, it further includes:

[0025] On the premise of satisfying the safety constraints of the 10kV area operation optimization model, adjust the line topology for optimization, and use the single-commodity flow method to ensure that the line topology satisfies the radial constraint:

[0026] Σz ij,s =n - 1

[0027]

[0028] -Mz ij,s ≤F ij,s ≤Mz ij,s

[0029] Among them, n is the number of nodes, is a 0 / 1 variable. When it is 0, it means that node i is not a source node, and when it is 1, it means that node i is a source node. F ij,s is the commodity flow flowing through line ij, and F jk,s is the commodity flow flowing through line jk.

[0030] As a preferred solution of the multi-voltage level collaborative operation optimization method considering new energy consumption according to the present invention, wherein: the safe operation constraints of the 110 kV area operation optimization model are the same as those of the 10 kV area. In addition, it is also necessary to meet the constraints of power reverse transmission to the superior power grid, that is:

[0031]

[0032] Among them, P 0,t,s represents the reverse transmission power of the 110 kV level, represents the upper limit of the reverse transmission power.

[0033] As a preferred solution of the multi-voltage level collaborative operation optimization method considering new energy consumption according to the present invention, wherein: the iterative solution of the optimization problem of the double-layer model includes:

[0034] Solving the optimization problem of the 10 kV level to obtain a set of optimal topological structures, and transferring the consumption results of the 10 kV level as boundary conditions to the 110 kV level to solve the optimization problem of the 110 kV level;

[0035] If the optimization problem of the 110 kV level can be solved, the operation mode of the 110 kV level that meets the safety constraints such as power reverse transmission to the large power grid is obtained, and the solution process ends; if it cannot be solved, the objective function of the 10 kV area is constrained and expressed as:

[0036]

[0037] Among them, C 0 is the maximum value obtained by solving the optimization problem of the 10 kV level, and β 1 and β 2 are constants;

[0038] During the iteration process, the constants α, β 1 and β 2 are restricted. As β 1After the adjustment of the number of steps, if the problem of the 110 kV level cannot be solved no matter how the network loss is adjusted under the current accommodation rate, the constraint will be β at this time. 2 Iteration of the step size. After the iteration, the accommodation rate needs to be changed and the network loss is adjusted again under the new accommodation rate until a feasible solution is found.

[0039] In a second aspect, the present invention provides a multi-voltage level collaborative operation optimization system considering new energy accommodation, including:

[0040] A data clustering module for clustering data of distributed power sources and load demands in the new energy output operation scenario.

[0041] An analysis and modeling module for establishing a single-voltage level operation optimization model considering new energy accommodation according to the clustering results, and analyzing the interaction effects between different voltage levels and the weight relationship between multi-dimensional economic indicators.

[0042] An iterative solution module for constructing a two-layer model for multi-voltage level operation optimization generated based on objective function constraints, and iteratively solving the optimization problem of the two-layer model to achieve collaborative operation optimization of multi-voltage levels.

[0043] In a third aspect, the present invention provides an electronic device, including:

[0044] A memory and a processor;

[0045] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-voltage level collaborative operation optimization method considering new energy accommodation are realized.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the multi-voltage level collaborative operation optimization method considering new energy accommodation are realized.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a multi-voltage level collaborative operation optimization method and system considering the new energy consumption. The clustering method based on DBSCAN and the improved K-means algorithm can improve the accuracy of the clustering results while taking into account the algorithm efficiency, and is suitable for processing high-dimensional, noisy and complex structure scene data; the operation optimization model considering the safe and economical consumption of new energy can comprehensively consider the mutual constraints of various factors, while ensuring the safe operation of the power grid, improve the consumption rate of new energy and reduce network losses, and improve the economy of distribution network operation; the designed solution algorithm is based on the interactive influence between multiple voltage levels and the iterative solution of different weights of economic indicators. In the scenario where there are many reconstruction schemes and large computational complexity, the effective convergence of feasible solutions is guaranteed, while the solution efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 A schematic diagram of the overall process logic of a multi-voltage level coordinated operation optimization method considering new energy consumption according to an embodiment of the present invention;

[0050] Figure 2 A flow chart of a scenario clustering method based on DBSCAN and an improved K-means clustering algorithm for a multi-voltage level coordinated operation optimization method considering new energy consumption according to an embodiment of the present invention;

[0051] Figure 3 This is a diagram of different topological structures of a 10kV multi-feeder distribution network according to a multi-voltage level coordinated operation optimization method considering new energy consumption described in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] Example 1

[0054] Reference Figure 1 - Figure 2An embodiment of the present invention provides an optimization method for coordinated operation of multiple voltage levels considering new energy consumption, as follows Figure 1 Specifically, it includes the following steps:

[0055] S100: Cluster the data of distributed power sources and load demands under the new energy output operation scenario;

[0056] S200: Establish an operation optimization model for a single voltage level considering new energy consumption based on the clustering results, and analyze the interaction effects between different voltage levels and the weight relationship between multi-dimensional economic indicators;

[0057] S300: Construct a two-layer model for operation optimization of multiple voltage levels generated based on objective function constraints, and iteratively solve the optimization problem of the two-layer model to achieve coordinated operation optimization of multiple voltage levels.

[0058] It should be noted that the present invention provides an optimization method and system for coordinated operation of multiple voltage levels considering new energy consumption. Based on the clustering method of DBSCAN and improved K-means algorithms, it can improve the accuracy of clustering results while taking into account the algorithm efficiency, and is suitable for processing scenario data with high dimensions, more noise and complex structures; the operation optimization model considering the safe and economic consumption of new energy can comprehensively consider the mutual restraint relationship of various factors, improve the consumption rate of new energy and reduce network losses while ensuring the safe operation of the power grid, and improve the economy of the distribution network operation; the designed solution algorithm iteratively solves based on the interaction effects between multiple voltage levels and different weights of economic indicators, and ensures the effective convergence of feasible solutions and improves the solution efficiency in scenarios with a large number of reconstruction schemes and large computational amounts.

[0059] In the embodiment of the present application, the above step S100 clustering the data of distributed power sources and load demands under the new energy output operation scenario includes:

[0060] After collecting the numerical values of distributed power sources and load demands at each moment in all scenarios, the DBSCAN algorithm can divide the regions with sufficiently high data density into clusters, that is, the largest set of density-connected data points, and can discover clusters of any shape in the spatial database with noise. The specific steps are as follows:

[0061] A1: For each point P in the dataset, calculate how many neighbors there are in its ε-neighborhood, where the threshold of the number of neighbors is usually defined by a parameter MinPts;

[0062] A2: If the number of points in the ε-neighborhood of a point is greater than or equal to MinPts, then this point is marked as a core point;

[0063] A3: For each core point, find all the points that are density-connected to it. If point P is in the ε-neighborhood of point O and O is a core point, then P is a point that is density-connected to O;

[0064] A4: Points that are not marked as core points are marked as noise points, and points that are density-connected to a certain core point but are not core points are marked as border points;

[0065] A5: Assign an independent cluster label to each core point or the points density-connected to it. If a point is density-connected to multiple core points, then it will be assigned the cluster label of the first found core point;

[0066] A6: All noise points form an independent cluster and are removed from the overall data.

[0067] Furthermore, the classical K-means algorithm requires manually setting the target number of clusters (the K value), and due to the random selection of the initial cluster centers without principle, the selected cluster centers may be too close, resulting in the clustering result falling into a local optimum. The improved K-means algorithm adopted in this embodiment can overcome the defect that the classical K-means algorithm is prone to falling into a local optimum and obtain a more accurate clustering result. As Figure 2 shown, the algorithm steps include:

[0068] B1: Randomly select 1 sample from the dataset as the initial cluster center C1;

[0069] B2: First, calculate the shortest distance between each sample and the currently existing cluster centers, denoted by D(i); then calculate the probability that each sample is selected as the next cluster center, expressed as:

[0070]

[0071] Finally, select the next cluster center according to the roulette method;

[0072] B3: Repeat step 2 until a total of K cluster centers C = {c 1 , c 2 , …, c k} are selected;

[0073] B4: For each sample x (i) in the dataset, calculate its distances to the K cluster centers and assign it to the class corresponding to the cluster center with the minimum distance;

[0074] B5: For each cluster center c j , recalculate its cluster center

[0075] B6: Repeat steps 4 and 5 until the positions of the clustering centers no longer change.

[0076] It should be noted that through clustering analysis in the above step S100, the most representative scenarios can be screened out, avoiding decision-making biases caused by data overload or insufficiency in traditional methods, while improving the generalization ability and prediction accuracy of the model. In addition, by adopting advanced clustering algorithms such as DBSCAN and improved K-means, it can automatically adapt to the characteristics of data distribution, accurately separate noise points, ensure that the clustering results are closer to the actual operating conditions, and thus provide strong support for achieving precise new energy consumption and efficient power grid dispatching.

[0077] In the embodiment of this application, the above step S200 includes:

[0078] For the operation optimization model of the 10kV area, in order to ensure that the new energy within the area can be maximally consumed while reducing the operating network losses to achieve the goal of economic operation of the power grid, the objective function of the optimization model is expressed as:

[0079]

[0080] where p s represents the probability of the occurrence of scenario s, Δt represents the simulated time interval, represents the new energy consumption power of node i at time t under scenario s, represents the loss power of line ij at time t under scenario s, and α represents a constant;

[0081] While realizing the economic consumption of new energy, in order to meet the safe operation requirements of the distribution network, a series of safety constraints of the model are:

[0082]

[0083] V i,min ≤V i,t,s ≤V i,max

[0084] (P ij,t,s ) 2 +(Q ij,t,s ) 2 ≤(S max ) 2

[0085]

[0086] I d ≤I dmax

[0087] where M is a sufficiently large constant, z ij,sA 0 / 1 variable representing the open / closed state of line ij. 0 indicates the line is open, and 1 indicates the line is connected, V i,t,s represents the voltage at node i, V j,t,s is the voltage at node j, P ij,t,s and Q ij,t,s respectively represent the active power and reactive power flowing through line ij, R ij and X ij respectively represent the resistance and reactance of line ij, I ij,t,s represents the current flowing through line ij, V i,min and V i,max are respectively the minimum voltage and maximum voltage at node i, S max represents the maximum apparent power that the line can pass through, is the total harmonic distortion rate of the s-phase voltage at node k in the distribution network, is the maximum total harmonic distortion rate, I d is the short-circuit current of the system, I dmax is the maximum short-circuit current value specified by the national standard. In the above formula, the first constraint means using the big M method to ensure that when the line is open, there is no direct connection between the voltages at both ends of the line. The second constraint ensures that the node voltage does not exceed the limit. The third constraint means that the line load rate does not exceed the upper limit. The fourth constraint means that the total harmonic distortion rate of the node voltage does not exceed the upper limit. The fifth constraint means that the short-circuit current does not exceed the allowable limit value.

[0088] On the premise of meeting the safety constraints of the 10kV regional operation optimization model, adjust the line topology for optimization, and use the single-commodity flow method to ensure that the line topology meets the radial constraint:

[0089] Σz ij,s =n - 1

[0090]

[0091] -M z ij,s ≤F ij,s ≤M z ij,s

[0092] where n is the number of nodes, is a 0 / 1 variable. When it is 0, it indicates that node i is not a source node, and when it is 1, it indicates that node i is a source node, F ij,s is the commodity flow flowing through line ij, F jk,s is the commodity flow flowing through line jk.

[0093] Furthermore, for the 110kV-level power grid, the absorption result of the 10kV level will be passed as a known condition to the 110kV level. Therefore, when optimizing the operation mode of the 110kV level, only the minimum network loss needs to be used as the objective function, expressed as:

[0094]

[0095] Among them, represents the network loss power of line ij at time t under scenario s.

[0096] For the 110 kV region, its safe operation constraints are in the same form as those of the 10 kV region, and the operation mode constraints are in the same form as the network topology constraints of the 10 kV region. However, on this basis, it is also necessary to satisfy the constraint of power reverse transmission to the superior power grid, that is:

[0097]

[0098] Among them, P 0,t,s represents the reverse transmission power at the 110 kV level, represents the upper limit of the reverse transmission power.

[0099] It should be noted that the above step S200 can not only accurately reflect the impact of the volatility and uncertainty of new energy output on each voltage level of the power grid, but also provide a scientific basis for decision-making by quantifying the importance of different economic indicators. In this way, the model can more effectively coordinate the matching between new energy generation and load demand, ensure the maximization of new energy utilization rate under the premise of meeting power supply reliability. At the same time, the research on the interactive influence between voltage levels helps to reveal the complex relationships within the system, promote the collaborative work between power grids at all levels, improve the overall operation efficiency and stability, and thus lay a solid foundation for realizing a more intelligent, flexible and economic power dispatching.

[0100] In the embodiment of the present application, the above step S300 includes the following sub-steps C1 - C3;

[0101] In C1: Solve the optimization problem of the 10 kV level to obtain a set of optimal topological structures;

[0102] In C2: Use the accommodation result of the 10 kV level as a boundary condition to be passed to the 110 kV level, and solve the optimization problem of the 110 kV level;

[0103] In C3: If the optimization problem of the 110 kV level can be solved, obtain the operation mode of the 110 kV level that satisfies the safety constraints such as power reverse transmission to the large power grid, and the solution process ends; if it cannot be solved, generate constraints for the objective function of the 10 kV region, which is expressed as:

[0104]

[0105] Among them, C 0 is the maximum value obtained by solving the optimization problem of the 10 kV level, β1 and β 2 are constants;

[0106] During the iteration process, constants α and β 1 and β 2 are restricted as follows:

[0107]

[0108] During the solution process after restricting the parameters, the constants on the right side of the constraint generation will be iterated according to the following process:

[0109] C 0 -β 1 -β 1 -…-β 1 -β 2 -β 1 -β 1 -…-β 1 -β 2 -β 1 -β 1 -…-β 1 …

[0110] Furthermore, since β 1 < β 2 , the constraint will first be adjusted with a smaller step size. At this time, since the order of magnitude of the network loss is smaller than that of the accommodation volume, the network loss will be adjusted first, and the accommodation rate will not be changed. After adjusting a certain number of times with β 1 as the step size, if the problem at the 110 kV level cannot be solved by adjusting the network loss anyway at the current accommodation rate, the constraint will be iterated with a step size of β 2 . After the iteration, since the constraint has been adjusted with a larger step size, the accommodation rate needs to be changed next and the network loss needs to be adjusted again at the new accommodation rate until a feasible solution is found.

[0111] It should be noted that the above step S300 can not only comprehensively consider the mutual influence between different voltage levels to ensure the coordination and consistency between levels, but also maximize the overall economic benefits on the premise of meeting the system stability and reliability. By accurately modeling multi-dimensional economic indicators and operation constraints, it can effectively balance multiple objectives such as new energy accommodation, load demand response, and power grid safe operation, providing a scientific and reasonable decision-making support mechanism. In addition, the iterative solution method enables the model to dynamically adapt to the real-time changes of the power system, continuously optimize the operation strategy, improve the flexibility and response speed of the system, and thus provide a strong technical support for realizing efficient and intelligent power dispatching and management.

[0112] The above is a schematic solution of the multi-voltage level collaborative operation optimization method considering new energy consumption in this embodiment. It should be noted that the technical solution of the system for multi-voltage level collaborative operation optimization considering new energy consumption belongs to the same concept as the above-mentioned multi-voltage level collaborative operation optimization method considering new energy consumption. For the details not described in detail in the technical solution of the multi-voltage level collaborative operation optimization system considering new energy consumption in this embodiment, reference can be made to the description of the technical solution of the multi-voltage level collaborative operation optimization method considering new energy consumption above.

[0113] This embodiment also provides a multi-voltage level collaborative operation optimization system considering new energy consumption, including:

[0114] A data clustering module, configured to cluster the data of distributed power sources and load demands in the new energy output operation scenario;

[0115] An analysis and modeling module, configured to establish a single-voltage level operation optimization model considering new energy consumption according to the clustering results, and analyze the interaction effects between different voltage levels and the weight relationship between multi-dimensional economic indicators;

[0116] An iterative solution module, configured to construct a two-layer model for multi-voltage level operation optimization generated based on objective function constraints, and iteratively solve the optimization problem of the two-layer model to achieve the collaborative operation optimization of multi-voltage levels.

[0117] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0118] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, operator networks, NFC (Near Field Communication) or other technologies. When the computer program is executed by the processor, it realizes the multi-voltage level collaborative operation optimization method considering new energy consumption. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0119] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method proposed in the above embodiment.

[0120] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0121] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0122] Embodiment 2

[0123] Refer to Figure 3 , based on the previous embodiment, this embodiment provides an application example of a multi-voltage level collaborative operation optimization method and system considering new energy consumption, in order to verify and illustrate the technical effects adopted in this method.

[0124] The proposed model and solution algorithm in this embodiment can be applied to a multi-voltage level power grid. The dispatching period is 24h, and each node of the node power grid is configured with distributed renewable energy units with different installed capacities. First, according to the scenario clustering method proposed by the present invention, data such as new energy output and load power within one year or multiple years are clustered into several scenarios. The specific steps are as Figure 2 shown. Secondly, the problem is solved according to the iterative solution method of the multi-voltage level operation optimization model based on constraint generation proposed by the present invention. The specific steps are as follows:

[0125] Step 1: Input the case information and give a set of initial operating conditions;

[0126] Step 2: The 10kV level power grid performs network topology optimization with economic optimality as the goal and security as the constraint, and transfers the positive / reverse power of the optimized 10kV level to the 110kV level.

[0127] Step 3: The 110 kV level takes the forward / backward power of the 10 kV level as the boundary condition, and optimizes the operation mode with the goal of the best economy and the constraint of safety.

[0128] Step 4: Judge whether there is a feasible solution to the optimization problem in Step 3. If there is, the optimal operation plan that meets the backward power constraint is obtained, and the solution process ends; if not, the constraint generation of the objective function is carried out.

[0129] Step 5: Make small-step changes to the constants on the right side of the constraint. Since the order of magnitude of the network loss energy is smaller than that of the absorbed energy, the current maximum feasible absorption rate is first ensured, the network loss is optimized, and it is verified whether there is a feasible solution at the 110 kV level under this optimization result. If not, it is judged whether a feasible solution at the 110 kV level can be found by adjusting the network loss at this absorption rate. If yes, Step 5 is executed again; if not, go to Step 6.

[0130] Step 6: Make large-step changes to the constants on the right side of the constraint, thereby changing the new energy absorption rate, and verify whether there is a feasible solution at the 110 kV level under this optimization result. If there is, the solution process ends; if not, return to Step 5.

[0131] Among them, different 10 kV network topology schemes can be obtained in the process of optimizing the network loss in Step 5. The network topology obtained at a certain new energy absorption rate is as Figure 3 shown. Name Figure 3 each scheme as Scheme 1 to Scheme 4 in turn. Table 1 gives the network losses under different network topology schemes.

[0132] Table 1: Network losses under different 10 kV network topologies.

[0133] Solution Network loss kWh Solution 1 147.9 Solution 2 142.1 Solution 3 140.5 Solution 4 139.6

[0134] As can be seen from the above, the multi-voltage level collaborative operation optimization method and system considering new energy absorption provided by the present invention, based on the clustering method of DBSCAN and improved K-means algorithms, can improve the accuracy of the clustering result while taking into account the algorithm efficiency, and is suitable for processing high-dimensional, noisy and complex-structured scenario data; the operation optimization model considering the safe and economic absorption of new energy can comprehensively consider the mutual restriction relationship of various factors, improve the absorption rate of new energy and reduce the network loss while ensuring the safe operation of the power grid, and improve the economy of the distribution network operation; the designed solution algorithm iteratively solves based on the interactive influence between multi-voltage levels and different weights of economic indicators, and ensures the effective convergence of feasible solutions and improves the solution efficiency in the scenario with a large number of reconstruction schemes and large computational workload.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0136] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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 code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0137] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0138] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide means for implementing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of functions specified in one or more boxes.

[0140] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A multi-voltage level coordinated operation optimization method considering new energy consumption, characterized in that: include: Clustering the data of distributed power sources and load demands in the renewable energy output operation scenario; According to the clustering results, a single voltage level operation optimization model considering the consumption of new energy is established, and the interactive impact between different voltage levels and the weight relationship between multi-dimensional economic indicators are analyzed; A two-layer model for multi-voltage level operation optimization generated based on objective function constraints is constructed, and the optimization problem of the two-layer model is iteratively solved to achieve coordinated operation optimization of multiple voltage levels.

2. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 1 is characterized in that: The clustering of the data of distributed power sources and load demands in the renewable energy output operation scenario includes: After collecting the values ​​of distributed power sources and load demands at all times in all scenarios, the DBSCAN algorithm is used to divide the areas with high data density into clusters, that is, the maximum set of density-connected data points, and find clusters of arbitrary shapes in the noisy spatial database. Then, the improved K-means algorithm is used to obtain more accurate clustering results.

3. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 2 is characterized in that: The establishment of a single voltage level operation optimization model considering new energy consumption includes: The economic optimal objective function of the 10kV regional operation optimization model is: Among them, p s represents the probability of scene s appearing, Δt represents the simulation time interval, represents the renewable energy consumption power of node i at time t under scenario s, represents the power loss of line ij at time t in scenario s, and α represents a constant; The economic optimal objective function of the 110kV regional operation optimization model is: in, Represents the network loss power of line ij at time t in scenario s.

4. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 3 is characterized in that: The safety constraints of the 10kV regional operation optimization model include: In i,min ≤V i,t,s ≤V i,max (P ij,t,s ) 2 +(Q ij,t,s ) 2 ≤(S max ) 2 I d ≤I dmax Where M is a sufficiently large constant, z ij,s is a 0 / 1 variable representing the disconnection status of line ij. 0 means the line is disconnected, and 1 means the line is connected. V i,t,s represents the voltage at node i, V j,t,s is the voltage at node j, P ij,t,s and Q ij,t,s Respectively represent the active power and reactive power flowing through line ij, R ij and X ij Represent the resistance and reactance of line ij, I ij,t,s Represents the current flowing through line ij, V i,min and V i,max are the minimum and maximum voltages of node i, respectively, S max Indicates the maximum apparent power that the line can pass. is the total harmonic distortion rate of the s-phase voltage at the k-node in the distribution network, is the maximum voltage total harmonic distortion rate, I d is the short-circuit current of the system, I dmax It is the maximum short-circuit current value specified by the national standard.

5. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 4 is characterized in that: Also includes: Under the premise of meeting the safety constraints of the 10kV regional operation optimization model, the line topology is adjusted for optimization, and the single commodity flow method is used to ensure that the line topology meets the radial constraints: Σz ij,s =n-1 Where n is the number of nodes, is a 0 / 1 variable. When it is 0, it means that node i is not a source node. When it is 1, it means that node i is a source node. ij,s is the commodity flow on route ij, F jk,s is the commodity flow on line jk.

6. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 5 is characterized in that: The safe operation constraints of the 110kV regional operation optimization model are consistent with those of the 10kV region. In addition, the constraints of reverse power transmission to the upper grid need to be met, namely: Among them, P 0,t,s Indicates the reverse power at the 110kV level. Indicates the upper limit of the reverse power.

7. The multi-voltage level coordinated operation optimization method considering new energy consumption as claimed in claim 6 is characterized in that: Iteratively solving the optimization problem of the two-layer model includes: Solve the optimization problem of the 10kV level to obtain a set of optimal topological structures, pass the 10kV level absorption results as boundary conditions to the 110kV level, and solve the optimization problem of the 110kV level; If the optimization problem of the 110kV level can be solved, the operation mode of the 110kV level that satisfies the safety constraints such as the reverse power transmission of the large power grid is obtained, and the solution process ends; if it cannot be solved, the objective function of the 10kV area is constrained and generated, which is expressed as: Among them, C0 is the maximum value obtained by solving the optimization problem at the 10kV level, and β1 and β2 are constants; During the iteration process, the constants α, β1 and β2 are restricted. After the number of adjustments is made with a step size of β1, if the 110kV level problem cannot be solved no matter how the network loss is adjusted under the current absorption rate, the constraint will be iterated with a step size of β2. After the iteration, the absorption rate needs to be changed and the network loss needs to be adjusted again under the new absorption rate until a feasible solution is found.

8. A system using the multi-voltage level coordinated operation optimization method considering new energy consumption as described in any one of claims 1 to 7, characterized in that: include: Data clustering module, used to cluster the data of distributed power sources and load demands in the scenario of renewable energy output operation; An analysis and modeling module is used to establish a single voltage level operation optimization model considering the consumption of new energy according to the clustering results, and to analyze the interactive impact between different voltage levels and the weight relationship between multi-dimensional economic indicators; The iterative solution module is used to construct a two-layer model for multi-voltage level operation optimization generated based on objective function constraints, and iteratively solve the optimization problem of the two-layer model to achieve coordinated operation optimization of multiple voltage levels.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-voltage level coordinated operation optimization method considering new energy consumption as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-voltage level coordinated operation optimization method considering new energy consumption as described in any one of claims 1 to 7.

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