Distribution network distributed operation optimization method and system based on complementary characteristic optimization algorithm

Abstract

The invention provides a power distribution network distributed operation optimization method and system based on a complementary characteristic optimization algorithm, and relates to the technical field of distributed power distribution network optimization, and the method comprises the steps: introducing a photovoltaic microgrid into a power distribution network in a distributed manner for a power distribution network topological structure, and constructing a distributed power distribution network power flow model; constructing a distributed power distribution network constraint condition and a photovoltaic micro-grid constraint condition based on a distributed power distribution network power flow model, and constructing a power distribution network distributed optimization operation model by taking minimum system cost and photovoltaic micro-grid power fluctuation suppression as objective functions; and the convex optimization algorithm is adopted to solve the distribution network distributed optimization operation model, optimal operation of the distributed distribution network is realized, and the overall stability and the power quality of the power grid can be maintained.

Description

Technical Field

[0001] The present disclosure relates to the technical field of distributed distribution network optimization, and in particular to a distribution network distributed operation optimization method system based on a complementary characteristic optimization algorithm. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] In recent years, with the rapid development of the economy, electricity consumption has also increased. In order to meet the electricity demand of residents, the development and utilization of various energy sources, especially renewable energy, has gradually increased. Moreover, with the increase in power transmission, the increase in information processing load and other factors, the sustainable development of the economy and all aspects will suffer losses. The inefficient utilization of traditional fossil energy is in urgent need of improvement. Some companies have increased their research efforts on the efficient utilization of traditional energy and the development and utilization of renewable energy and clean energy. Research on wind power and energy storage systems is becoming increasingly mature.

[0004] With the increasing power consumption year by year, the traditional power grid structure has gradually formed a large-capacity and large-unit power generation, long-distance ultra-high voltage and even ultra-high voltage ultra-large interconnected power grid system. This has led to excessive dependence on the power supply of the receiving end, which has caused a high risk of stability and anti-interference of the power grid. The large power grid is less sensitive to load changes. As the transmission distance increases, the power supply capacity to marginal areas decreases. In addition, in large power grids, faults are more likely to affect more areas, resulting in large-scale power outages, causing significant economic and life safety losses and serious social impacts. Distributed power generation is closer to the load center, and the stability of power supply in remote areas is improved. It can also reduce the construction and operating costs of transmission and transformation lines, transformers and related equipment. The line loss is small, which helps to efficiently utilize resources and reduce the occurrence of environmental problems. Because of its small scale and easy construction, the resource allocation is more economical and reasonable.

[0005] However, since distributed generation has relatively more clean energy, has greater random fluctuations, and has relatively poor stability when connected to the large power grid, it will lead to problems of reduced power quality and stability when connected to the grid. The parallel coordinated operation performance of distributed generation and the large power grid is poor, and flexible grid operation cannot be achieved. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a distribution network distributed operation optimization method system based on a complementary characteristic optimization algorithm. By constructing mathematical models for various parts of the microgrid and the main power grid, optimization objectives are proposed, and an optimization algorithm with energy storage complementary characteristics is designed. Through multi-objective solving, the best solution for capacity optimization configuration is obtained, and it is verified based on the IEEE33 node model.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: The distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm includes: According to the topological structure of the distribution network, the photovoltaic microgrid is introduced into the distribution network in a distributed manner to build a distributed distribution network power flow model; Based on the distributed distribution network power flow model, the distributed distribution network constraints and photovoltaic microgrid constraints are constructed, and the distributed optimization operation model of the distribution network is constructed with the lowest system cost and the suppression of photovoltaic microgrid power fluctuations as the objective function; The distributed optimization operation model of the distribution network is solved to achieve the optimal operation of the distributed distribution network.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: The distributed operation optimization system of the distribution network based on the complementary characteristic optimization algorithm includes: The model building module is used to introduce the photovoltaic microgrid into the distribution network in a distributed manner according to the distribution network topology and build a distributed distribution network power flow model; The objective function construction module is used to construct the distributed distribution network constraints and photovoltaic microgrid constraints based on the distributed distribution network power flow model, and to construct the distributed optimization operation model of the distribution network with the lowest system cost and the suppression of photovoltaic microgrid power fluctuation as the objective function; The model solving module is used to solve the distributed optimization operation model of the distribution network to achieve the optimal operation of the distributed distribution network.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the distributed operation optimization method of a distribution network based on a complementary characteristic optimization algorithm is implemented.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm is implemented.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the distribution network distributed operation optimization method based on the complementary characteristic optimization algorithm.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The disclosed distribution network distributed operation optimization method based on complementary characteristic optimization algorithm uses the lowest cost as the objective function to obtain the flow calculation equation; according to the constructed flow calculation equation, introduces the distribution network power balance, rotating standby, generator output and other restrictions to obtain the distribution network constraint conditions for flow calculation; according to the constructed flow calculation equation, introduces the microgrid output, climbing, output balance and other restrictions to obtain the microgrid constraint conditions for flow calculation; according to the revised distribution network distributed optimization operation model, obtains the flow calculation results. It can effectively guarantee the convergence of data, avoid the non-convergence problem existing in repeated iterations, improve the optimization efficiency, and promote the improvement of the current power grid economic dispatching level.

[0013] The distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm disclosed in the present invention has good followability in that the charging and discharging time and power of the energy storage system change with the random new energy power source, plays a good peak shaving and valley filling effect, improves the power supply reliability of the power grid, reduces the fluctuation influence and strong impact on the power grid caused by the randomness of power generation of random new energy power sources such as photovoltaic power generation and wind power generation, and helps to maintain the overall stability and power quality of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0015] Figure 1 The solution algorithm flow of the embodiment of the present disclosure; Figure 2 A trend diagram showing that the objective function of the embodiment of the present disclosure gradually decreases as the number of iterations increases; Figure 3 A graph showing the charging and discharging time and power of the energy storage system of the embodiment of the present disclosure from 19:00 to 22:00 as the random new energy power source changes; Figure 4 A graph showing the charging and discharging time and power of the energy storage system at 20:00-21:00 as the random new energy power source changes; Figure 5 This is a graph showing how the charging and discharging time and power of the energy storage system at 9, 11, 18, and 20-11 hours change with the random new energy power source. DETAILED DESCRIPTION

[0016] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0017] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0018] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0019] Example 1 In one embodiment of the present disclosure, a method for optimizing distributed operation of a distribution network based on a complementary characteristic optimization algorithm is provided, comprising: Step 1: Based on the distribution network topology, the photovoltaic microgrid is introduced into the distribution network in a distributed manner to build a distributed distribution network power flow model; Step 2: Based on the distributed distribution network power flow model, the distributed distribution network constraints and photovoltaic microgrid constraints are constructed, and the distributed optimization operation model of the distribution network is constructed with the lowest system cost and the suppression of photovoltaic microgrid power fluctuations as the objective function; Step 3: Use convex optimization algorithm to solve the distributed optimization operation model of distribution network and realize the optimal operation of distributed distribution network.

[0020] As an embodiment, the specific implementation process of the distribution network distributed operation optimization method based on the complementary characteristic optimization algorithm disclosed in the present invention is as follows: Step 1: According to the topology of the distribution network, the photovoltaic microgrid is introduced into the distribution network in a distributed manner to build a distributed distribution network power flow model; Specifically, the DistFlow distributed distribution network power flow model is constructed based on the distribution network power flow equation in the form of network flow, as follows: (1) (2) (3) (4) in, and are the active power and reactive power of the distributed generation at node i respectively; and are the active load and reactive load at node i respectively; , , , and are the active power, reactive power, square of current amplitude, resistance and inductive reactance on line (i, j) respectively; is the square of the voltage amplitude at node i.

[0021] Step 2: In the distributed distribution network power flow model, the distributed distribution network constraints and photovoltaic microgrid constraints are constructed, and the distributed optimization operation model of the distribution network is constructed with the lowest system cost and the suppression of photovoltaic microgrid power fluctuations as the objective function; Specifically, this model focuses on cost optimization, with the goal of minimizing the operating cost of the entire system. The objective function is expressed as follows: (5) According to the randomness of renewable energy output, reasonable configuration of energy storage system capacity can make the grid work more stable and reduce power fluctuations. Reasonable configuration of energy storage system capacity is to obtain the objective function of the power fluctuation suppression characteristics of the photovoltaic microgrid energy storage system by analyzing the difference in power changes: (6) In the formula, is the power generated by renewable energy during period t, is the output power after compensation during period t; T It is the scheduling period.

[0022] In summary, the overall objective function is: (7) in, F is the operating cost of the entire system, It is the operating cost of the distribution network, including the operating cost of the generators in the distribution network and the income from supplying electricity to the outside; is the operating cost of the jth microgrid, including the operating cost of distributed power sources such as wind turbines, photovoltaic cells, and batteries in the microgrid, the charging cost, and the income from external power supply. m is the number of microgrids, m=3 in this model. , are the weights of each target.

[0023] Furthermore, the constraints for constructing a distributed distribution network are as follows: 1. Objective function: (8) (9) (10) 2. Power balance of distribution network

[0024] (11) 3. Generator output limitation (12) 4. Climbing limit

[0025] (13) 5. Distribution network spinning reserve (14) 6. Tie line power limitation (15) in n is the number of generators in the distribution network, is the first i The effective output of a generator. , , is the operating cost coefficient of the generator, Peak and valley electricity prices. For time t The predicted load, , For the distribution network i The upper and lower constraints on the active output of each generator. is the ramp rate of the i-th generator in the distribution network, For the moment t Standby demand value of the distribution network. and For distribution network and j The upper and lower constraints on power exchange between microgrids.

[0026] Furthermore, the constraints for constructing a photovoltaic microgrid are as follows: 1. Objective Function (16) (17) (18) (19) 2. Microgrid generator output limitation (20) 3. Microgrid climbing limit (twenty one) 4. Microgrid power balance (twenty two) 5. Tie line power limitation (twenty three) 6. Energy storage charging and discharging constraints (twenty four) 7. The energy storage charging and discharging power constraints are (25) in, is the number of nodes in the jth microgrid i The output of the generator, , , is the cost coefficient. k is the battery cost factor. , is the charge and discharge price of the battery. For the i The generator is j The ramp rate in a microgrid. , The nodes are The charge and discharge status of the energy storage device, its value is 0 or 1. , They are Cycle Node The charging and discharging power of the energy storage device and its upper and lower limits; , are the upper and lower limits of the charging and discharging power of the energy storage device at node $i$ respectively.

[0027] Step 3: Use convex optimization algorithm to solve the distributed optimization operation model of the distribution network and realize the optimal operation of the distributed distribution network.

[0028] Specifically, Figure 1 As shown, the present disclosure constructs the DistFlow power flow model and relaxes the equations therein to make it more suitable for solving by convex optimization algorithms, which facilitates the convergence and effectiveness of power flow calculations. The specific solution process is as follows: Initialize parameters, determine penalty factor, initial value of Lagrange multiplier and system parameters, and set the number of iterations k=1; Decompose the original problem according to the tie line power and determine the Lagrangian function, optimize the sub-problems and transfer the coupling variables to the adjacent sub-problems for iteration; When the preset accuracy requirement is reached, the calculation stops; the calculation results are output and used to achieve the optimal operation of the distributed distribution network; If the preset accuracy requirement is not met, set k=k+1, update the number of iterations, re-optimize the subproblem and pass the coupling variables to the adjacent subproblem for iteration.

[0029] As an example, Figure 2It means that the objective function gradually decreases with the increase of the number of iterations, and finally converges to a final value that satisfies the accuracy after 77 iterations. The results show that the method proposed in the present disclosure is convergent and effective.

[0030] As an example, Figure 3-Figure 5 In the figure, Pch1, Pch2, and Pch3 are the charging power of the energy storage system, i.e., the battery in microgrids 1, 2, and 3, respectively; Pdis1, Pdis2, and Pdis3 are the discharging power of the battery in microgrids 1, 2, and 3, respectively; PV1, PV2, and PV3 are the photovoltaic power generation in microgrids 1, 2, and 3, respectively; and PW1, PW2, and PW3 are the wind power generation in microgrids 1, 2, and 3, respectively. It can be seen that the battery charging and discharging are balanced during the day, and the charging and discharging time and power of the energy storage system have good followability as the random new energy power source changes, and play a good peak-shaving and valley-filling effect, that is, when photovoltaic power generation and wind power generation output electricity, it is stored, and when photovoltaic power generation and wind power generation stop, electricity is output. Such operating characteristics have many advantages, the most important of which is to improve the power supply reliability of the power grid, reduce the fluctuation influence and strong impact on the power grid caused by the randomness of the power generation of random new energy power sources such as photovoltaic power generation and wind power generation, and help maintain the overall stability and power quality of the power grid.

[0031] Example 2 In one embodiment of the present disclosure, a distribution network distributed operation optimization system based on a complementary characteristic optimization algorithm is provided, comprising: The model building module is used to introduce the photovoltaic microgrid into the distribution network in a distributed manner according to the distribution network topology and build a distributed distribution network power flow model; The objective function construction module is used to construct the distributed distribution network constraints and photovoltaic microgrid constraints based on the distributed distribution network power flow model, and to construct the distributed optimization operation model of the distribution network with the lowest system cost and the suppression of photovoltaic microgrid power fluctuation as the objective function; The model solving module is used to solve the distributed optimization operation model of the distribution network using a convex optimization algorithm to achieve the optimal operation of the distributed distribution network.

[0032] Example 3 In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm is implemented.

[0033] Example 4 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the distribution network distributed operation optimization method based on the complementary characteristic optimization algorithm is implemented.

[0034] Example 5 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the distribution network distributed operation optimization method based on the complementary characteristic optimization algorithm.

[0035] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A distributed operation optimization method for a distribution network based on a complementary characteristic optimization algorithm, characterized in that: include: According to the topological structure of the distribution network, the photovoltaic microgrid is introduced into the distribution network in a distributed manner to build a distributed distribution network power flow model; Based on the distributed distribution network power flow model, the distributed distribution network constraints and photovoltaic microgrid constraints are constructed, and the distributed optimization operation model of the distribution network is constructed with the lowest system cost and the suppression of photovoltaic microgrid power fluctuations as the objective function; The distributed optimization operation model of the distribution network is solved to achieve the optimal operation of the distributed distribution network.

2. The distributed operation optimization method of distribution network based on complementary characteristic optimization algorithm according to claim 1, characterized in that: The distribution network power flow model based on network flow is as follows: in, and are the active power and reactive power of the distributed generation at node i respectively; and Node i Active load and reactive load at the location; , , , and They are respectively the active power, reactive power, square of current amplitude, resistance and inductive reactance values ​​on the line; For Node i The square of the voltage amplitude at .

3. The distributed operation optimization method of distribution network based on complementary characteristic optimization algorithm according to claim 1, characterized in that: The operating cost of the entire system is the lowest, and the objective function is as follows: By analyzing the differences in power changes, the objective function of the power fluctuation suppression characteristics of the photovoltaic microgrid energy storage system is obtained. In the formula, yes t The power generated by renewable energy during the period, yes t Output power after time period compensation; T It is the scheduling period. The overall objective function is: Where F is the operating cost of the entire system, It is the operating cost of the distribution network, including the operating cost of the generators in the distribution network and the income from supplying electricity to the outside; It is j The operating costs of a microgrid include the operating costs of wind turbines, photovoltaic cells, and distributed power sources of batteries in the microgrid, charging costs, and income from external power supply. m is the number of microgrids, , are the weights of each target.

4. The distributed operation optimization method of distribution network based on complementary characteristic optimization algorithm according to claim 1, characterized in that: Distributed distribution network constraints include distribution network power balance, generator output limitation, ramp limitation, distribution network spinning reserve and tie line power limitation.

5. The method for optimizing the distributed operation of a distribution network based on a complementary characteristic optimization algorithm according to claim 1, characterized in that: The constraints of the photovoltaic microgrid are microgrid generator output limitation, microgrid climbing limitation, microgrid power balance, interconnection line power limitation, energy storage charging and discharging constraint, and energy storage charging and discharging power constraint.

6. The distributed operation optimization method of distribution network based on complementary characteristic optimization algorithm according to claim 1, characterized in that: A convex optimization algorithm is used to solve the distributed optimization operation model of the distribution network, including initializing parameters, determining penalty factors, initial values ​​of Lagrange multipliers and system parameters; decomposing the original problem according to the tie-line power and determining the Lagrangian function, optimizing the sub-problems and transferring the coupling variables to adjacent sub-problems, and iterating. When the preset accuracy requirements are met, the calculation results are output and the optimal operation of the distributed distribution network is achieved using the calculation results.

7. A distribution network distributed operation optimization system based on complementary characteristic optimization algorithm, characterized in that: include: The model building module is used to introduce the photovoltaic microgrid into the distribution network in a distributed manner according to the distribution network topology and build a distributed distribution network power flow model; The objective function construction module is used to construct the distributed distribution network constraints and photovoltaic microgrid constraints based on the distributed distribution network power flow model, and to construct the distributed optimization operation model of the distribution network with the lowest system cost and the suppression of photovoltaic microgrid power fluctuation as the objective function; The model solving module is used to solve the distributed optimization operation model of the distribution network to achieve the optimal operation of the distributed distribution network.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the distributed operation optimization method of the distribution network based on the complementary characteristic optimization algorithm as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the distribution network distributed operation optimization method based on the complementary characteristic optimization algorithm as described in any one of claims 1 to 6.

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