Micro-grid optimal configuration method based on self-adaption

Abstract

The invention discloses a micro-grid optimal configuration method based on self-adaption, and belongs to the technical field of micro-grids, the micro-grid optimal configuration method comprises a control terminal unit, the control terminal unit is electrically connected with an analysis and modeling unit, the control terminal unit is in signal connection with an algorithm unit, and the control terminal unit is in signal connection with an optimal configuration unit. The optimal configuration unit is in signal connection with an algorithm unit, the control terminal unit is in signal connection with a data guarantee unit, and the data guarantee unit is in signal connection with an analysis and modeling unit. According to the invention, the distributed energy in the microgrid is scheduled and optimized in real time through the adaptive fast particle swarm algorithm and the adaptive differential whale optimization algorithm of the selection module in the algorithm unit, and the maximum utilization of the energy is ensured. The algorithms can be flexibly adjusted according to real-time energy production conditions and load requirements, energy waste is avoided, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of microgrids, and in particular to a microgrid optimization configuration method based on self-adaptation. Background Art

[0002] A microgrid is a small, independent power system, usually composed of distributed power sources (such as photovoltaic power sources, wind turbines, etc.), energy storage systems, loads, and monitoring and protection devices. It can achieve self-control, self-management, and self-protection. It can be connected to the external power grid and can also operate in an isolated island when the external power grid fails to ensure the power supply of key loads. However, the existing microgrids have low energy utilization, and low continuity and stability of power supply. Summary of the invention

[0003] The purpose of the present invention is to provide a microgrid optimization configuration method based on adaptation to solve the problems mentioned in the above background technology that the existing microgrid has low energy utilization rate and low continuity and stability of power supply.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for optimizing configuration of a microgrid based on self-adaptation, comprising a control terminal unit, the control terminal unit is electrically connected to an analysis and modeling unit, the control terminal unit is signal-connected to an algorithm unit, the control terminal unit is signal-connected to an optimization configuration unit, the optimization configuration unit is signal-connected to the algorithm unit, the control terminal unit is signal-connected to a data security unit, the data security unit is signal-connected to an analysis and modeling unit, the control terminal unit is electrically connected to a monitoring unit, and the control terminal unit is electrically connected to an implementation unit;

[0005] The control terminal unit comprises: S1: capable of directly controlling one or more devices to ensure that these devices operate according to predetermined programs or instructions;

[0006] The analysis and modeling unit includes: S2: evaluating data of various resources and establishing corresponding models;

[0007] The optimization configuration unit includes: S4: continuously optimizing and adjusting the configuration scheme of the microgrid according to the evaluation results to improve its operating efficiency and economy;

[0008] The data assurance unit includes: S5: responsible for managing and maintaining data elements, ensuring the atomicity, integrity and derivability of data;

[0009] As a further preferred embodiment of the present technical solution: the analysis and modeling unit includes a load characteristic analysis module, a power generation unit modeling module, and an energy storage modeling module, and the analysis and modeling unit is signal-connected to the load characteristic analysis module, the power generation unit modeling module, and the energy storage modeling module;

[0010] The load characteristic prediction model formula can be expressed as:

[0011] L t =αL t-1 +βL t-2 +γ∈ t :

[0012] in:

[0013] α, β, γ are model parameters, ∈ t is the error term;

[0014] As a further preferred embodiment of the present technical solution: the algorithm unit comprises a selection module, a design module, and a parameter adjustment module, and the algorithm unit is electrically connected to the selection module, the design module, and the parameter adjustment module;

[0015] As a further preferred embodiment of the present technical solution: the optimization configuration unit includes a solution module and an evaluation module, and the optimization configuration unit is electrically connected to the solution module and the evaluation module;

[0016] As a further preferred embodiment of the present technical solution: the data security unit comprises a data acquisition unit and a storage module, and the data acquisition unit is electrically connected to the storage module;

[0017] As a further preferred embodiment of the technical solution: the selection module includes an adaptive fast particle swarm algorithm and an adaptive differential whale optimization algorithm;

[0018] As a further preferred embodiment of the present technical solution: the design module includes initializing the population, generating random solutions, evaluating the quality of the solutions, and updating the position and speed of the solutions;

[0019] As a further preferred embodiment of the present technical solution: the load characteristic analysis module includes analyzing the size, time period distribution and volatility of the load;

[0020] As a further preferred embodiment of the present technical solution: the energy storage modeling module models the charging and discharging operations in the storage module;

[0021] The remaining energy formula of the energy storage modeling module is:

[0022] E(t)=E(t-1)+P charge (t)η c Δt-P discharge (t) / η d Δt;

[0023] P charge is the charging power;

[0024] P discharge is the discharge power;

[0025] η c and η d They are charge and discharge efficiency;

[0026] Δt is the time step;

[0027] As a further preferred embodiment of the present technical solution: the selection module, the design module, and the parameter adjustment module are electrically connected to a processing chip in the control terminal unit.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. In the present invention, the adaptive fast particle swarm algorithm and the adaptive differential whale optimization algorithm of the selection module in the algorithm unit are used to perform real-time scheduling and optimization of distributed energy in the microgrid to ensure the maximum utilization of energy. These algorithms can be flexibly adjusted according to the real-time energy production situation and load demand, avoiding energy waste and improving energy utilization efficiency;

[0030] 2. In the present invention, by optimizing the configuration units, it is possible to achieve complementarity and coordinated operation among multiple energy sources. When the output of a certain energy source decreases due to weather, it can automatically switch to other energy sources (such as wind energy or energy storage system) to ensure the continuity and stability of power supply;

[0031] 3. In the present invention, through the algorithm unit and the optimization configuration unit, the microgrid can respond quickly when a fault or abnormal situation is detected, switch to the backup power supply or adjust the operation strategy to minimize the power outage time and impact range. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 1 ;

[0033] Figure 2 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 2 ;

[0034] Figure 3 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 3 ;

[0035] Figure 4 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 4 ;

[0036] Figure 5 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 5 ;

[0037] Figure 6 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 6 ;

[0038] Figure 7 The present invention is a process of a microgrid optimization configuration method based on self-adaptation Figure 7 .

[0039] In the figure: 1. Control terminal unit; 2. Analysis and modeling unit; 3. Algorithm unit; 4. Optimization configuration unit; 5. Data assurance unit; 6. Monitoring unit; 7. Implementation unit; 201. Load characteristic analysis module; 202. Power generation unit modeling module; 203. Energy storage modeling module; 301. Selection module; 302. Design module; 303. Parameter adjustment module; 401. Solution module; 402. Evaluation module; 501. Data acquisition unit; 502. Storage module. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example

[0042] See also Figure 1-Figure 7 As shown, the present invention provides a technical solution: a microgrid optimization configuration method based on self-adaptation, comprising a control terminal unit 1, the control terminal unit 1 is electrically connected to an analysis and modeling unit 2, the control terminal unit 1 is signal-connected to an algorithm unit 3, the control terminal unit 1 is signal-connected to an optimization configuration unit 4, the optimization configuration unit 4 is signal-connected to the algorithm unit 3, the control terminal unit 1 is signal-connected to a data assurance unit 5, the data assurance unit 5 is signal-connected to the analysis and modeling unit 2, the control terminal unit 1 is electrically connected to a monitoring unit 6, the control terminal unit 1 is electrically connected to an implementation unit 7, the distributed energy in the microgrid is real-time dispatched and optimized through the adaptive fast particle swarm algorithm and the adaptive differential whale optimization algorithm of the selection module 301 in the algorithm unit 3, to ensure the maximum utilization of energy, these algorithms can be flexibly adjusted according to the real-time energy production situation and load demand, to avoid energy waste, and to improve energy utilization efficiency.

[0043] In this embodiment, specifically: the analysis and modeling unit 2 includes a load characteristic analysis module 201, a power generation unit modeling module 202, and an energy storage modeling module 203. The signals on the analysis and modeling unit 2 are connected to the load characteristic analysis module 201, the power generation unit modeling module 202, and the energy storage modeling module 203. The energy storage modeling module 203 plays an important role in the microgrid, and it is necessary to establish a dynamic loss model of the energy storage system to reflect the energy loss and efficiency changes during its charging and discharging process.

[0044] In this embodiment, specifically: the algorithm unit 3 includes a selection module 301 , a design module 302 , and a parameter adjustment module 303 , and the algorithm unit 3 is electrically connected to the selection module 301 , the design module 302 , and the parameter adjustment module 303 .

[0045] In this embodiment, specifically: the optimization configuration unit 4 includes a solution module 401 and an evaluation module 402. The optimization configuration unit 4 is electrically connected to the solution module 401 and the evaluation module 402. The evaluation module 402 can evaluate the optimized configuration scheme obtained by solution and analyze its performance and effect in different scenarios.

[0046] In this embodiment, specifically: the data security unit 5 includes a data acquisition unit 501 and a storage module 502 , and the data acquisition unit 501 is electrically connected to the storage module 502 .

[0047] In this embodiment, specifically: the selection module 301 includes an adaptive fast particle swarm algorithm and an adaptive differential whale optimization algorithm.

[0048] In this embodiment, specifically: the design module 302 includes initializing the population, generating random solutions, evaluating the quality of the solutions, and updating the position and speed of the solutions.

[0049] In this embodiment, specifically: the load characteristic analysis module 201 includes analyzing the size, time distribution, and volatility of the load to determine the power demand of the microgrid.

[0050] In this embodiment, specifically: the energy storage modeling module 203 models the charging and discharging operations in the storage module 502 .

[0051] In this embodiment, specifically: the selection module 301 , the design module 302 , and the parameter adjustment module 303 are electrically connected to the processing chip in the control terminal unit 1 .

[0052] Working principle or structural principle: First, the data security unit 5 will collect the data in the microgrid through the data acquisition unit 501, and then transmit the data to the control terminal unit 1, and the control terminal unit 1 will transmit the data to the analysis and modeling unit 2. The load characteristic analysis module 201 in the analysis and modeling unit 2 analyzes the load characteristics of the microgrid, including the size, time distribution, volatility, etc. of the load, so as to determine the power demand of the microgrid. Then the power generation unit modeling module 202 establishes an output model for each power generation unit in the microgrid, such as wind power generation, photovoltaic power generation, diesel engine power generation, etc., to reflect its power generation capacity under different conditions. At the same time, the control terminal unit 1 will The collected data is transmitted to the optimization configuration unit 4. The solution module 401 in the optimization configuration unit 4 inputs the optimization objective function and the constraint conditions into the selection module 301 in the algorithm unit 3. The optimal configuration scheme is obtained through algorithm iteration. Then the evaluation module 402 evaluates the solved optimization configuration scheme and analyzes its performance and effect in different scenarios to ensure that the scheme can meet the actual operation requirements of the microgrid. At the same time, the monitoring unit 6 will monitor the operation status of the microgrid in real time, collect operation data and analyze its performance. Finally, the implementation unit 7 will perform resource allocation and operation scheduling for the microgrid according to the optimization configuration scheme solved by the algorithm unit 3.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing microgrid configuration based on self-adaptation, comprising a control terminal unit (1), characterized in that: The control terminal unit (1) is electrically connected to an analysis and modeling unit (2), the control terminal unit (1) is signal-connected to an algorithm unit (3), the control terminal unit (1) is signal-connected to an optimization configuration unit (4), the optimization configuration unit (4) is signal-connected to the algorithm unit (3), the control terminal unit (1) is signal-connected to a data assurance unit (5), the data assurance unit (5) is signal-connected to the analysis and modeling unit (2), the control terminal unit (1) is electrically connected to a monitoring unit (6), and the control terminal unit (1) is electrically connected to an implementation unit (7); The control terminal unit (1) comprises: S1: capable of directly controlling one or more devices to ensure that these devices operate according to predetermined programs or instructions; The analysis and modeling unit (2) includes: S2: evaluating data of various resources and establishing corresponding models; The optimization configuration unit (4) comprises: S4: according to the evaluation results, continuously optimizing and adjusting the configuration scheme of the microgrid to improve its operating efficiency and economy; The data assurance unit (5) comprises: S5: responsible for managing and maintaining data elements, ensuring the atomicity, integrity and derivability of data.

2. The method for optimizing microgrid configuration based on self-adaptation according to claim 1, characterized in that: The analysis and modeling unit (2) comprises a load characteristic analysis module (201), a power generation unit modeling module (202), and an energy storage modeling module (203); the analysis and modeling unit (2) is signal-connected to the load characteristic analysis module (201), the power generation unit modeling module (202), and the energy storage modeling module (203); The load characteristic prediction model formula can be expressed as: L t =αL t-1 +βL t-2 +γ∈ t : in: α, β, γ are model parameters, ∈ t is the error term.

3. The method for optimizing microgrid configuration based on self-adaptation according to claim 2, characterized in that: The algorithm unit (3) comprises a selection module (301), a design module (302), and a parameter adjustment module (303); the algorithm unit (3) is electrically connected to the selection module (301), the design module (302), and the parameter adjustment module (303).

4. The method for optimizing microgrid configuration based on self-adaptation according to claim 3, characterized in that: The optimization configuration unit (4) comprises a solution module (401) and an evaluation module (402), and the optimization configuration unit (4) is electrically connected to the solution module (401) and the evaluation module (402).

5. The method for optimizing microgrid configuration based on self-adaptation according to claim 4, characterized in that: The data security unit (5) comprises a data acquisition unit (501) and a storage module (502), and the data acquisition unit (501) is electrically connected to the storage module (502).

6. The method for optimizing microgrid configuration based on self-adaptation according to claim 5, characterized in that: The selection module (301) includes an adaptive fast particle swarm algorithm and an adaptive differential whale optimization algorithm.

7. The method for optimizing microgrid configuration based on self-adaptation according to claim 6, characterized in that: The design module (302) includes initializing the population, generating random solutions, evaluating the quality of the solutions, and updating the positions and speeds of the solutions.

8. The method for optimizing microgrid configuration based on self-adaptation according to claim 7, characterized in that: The load characteristic analysis module (201) includes analyzing the size, time period distribution and volatility of the load.

9. The method for optimizing microgrid configuration based on self-adaptation according to claim 8, characterized in that: The energy storage modeling module (203) models the charging and discharging operations in the storage module (502); The remaining energy formula of the energy storage modeling module is: E(t)=E(t-1)+P charge (t)η c Δt-P discharge (t) / η d Δt; P charge is the charging power; P discharge is the discharge power; η c and η d They are charge and discharge efficiency; Δt is the time step.

10. The method for optimizing microgrid configuration based on self-adaptation according to claim 9, characterized in that: The selection module (301), the design module (302), and the parameter adjustment module (303) are electrically connected to a processing chip in the control terminal unit (1).

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