Multi-energy complementation and collaborative optimization method for micro-grid

Abstract

The invention discloses a multi-energy complementation and collaborative optimization method for a micro-grid, and belongs to the technical field of micro-grid collaborative optimization, and the method comprises the following steps: obtaining the predicted load power of the micro-grid and the time corresponding to the predicted load power; normalizing the predicted load power and the time corresponding to the predicted load power, and inputting the normalized predicted load power and time into N pre-trained multi-energy complementary and collaborative optimization models to obtain N multi-energy complementary and collaborative optimization schemes; if the N multi-energy complementary and collaborative optimization schemes are the same, directly outputting the multi-energy complementary and collaborative optimization schemes; and if the N multi-energy complementary and collaborative optimization schemes are different, obtaining corresponding credibility of the N multi-energy complementary and collaborative optimization schemes, and obtaining a final multi-energy complementary and collaborative optimization scheme by using the credibility. According to the method, multi-energy complementation and collaborative optimization of the micro-grid can be quickly and accurately realized in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid collaborative optimization, and more specifically to a multi-energy complementarity and collaborative optimization method for a microgrid. Background Art

[0002] With the continuous increase in energy demand and the limitations of energy supply, such as the limited fossil fuel resources and increasingly serious environmental problems, new energy systems have gradually attracted attention. As an important form of energy system, microgrids have the characteristics of decentralized power supply, renewable energy utilization and flexible adjustment, and have become an important means to achieve sustainable energy development. However, when introducing multi-energy systems in microgrids, the coordination and optimization control of multiple energy sources has become an important research direction.

[0003] Therefore, how to provide a multi-energy complementarity and collaborative optimization method for a microgrid that can realize multi-energy complementarity and collaborative optimization of the microgrid in real time, quickly and accurately is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a multi-energy complementarity and collaborative optimization method for a microgrid

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A multi-energy complementarity and collaborative optimization method for a microgrid, comprising the following steps:

[0007] Obtaining the predicted load power of the microgrid and the time corresponding to the predicted load power;

[0008] The predicted load power and the time corresponding to the predicted load power are normalized and then input into N pre-trained multi-energy complementation and collaborative optimization models to obtain N multi-energy complementation and collaborative optimization solutions;

[0009] If the N multi-energy complementarity and collaborative optimization schemes are the same, then the multi-energy complementarity and collaborative optimization schemes are directly output;

[0010] If the N multi-energy complementarity and collaborative optimization schemes are different, the corresponding credibility of the N multi-energy complementarity and collaborative optimization schemes is obtained, and the final multi-energy complementarity and collaborative optimization scheme is obtained using the credibility.

[0011] Preferably, the microgrid includes solar photovoltaic, wind power generation, diesel power generation and energy storage system.

[0012] Preferably, N pre-trained multi-energy complementarity and collaborative optimization models are obtained based on the following steps:

[0013] Obtain the historical load power of the microgrid and the time corresponding to the historical load power;

[0014] The historical load power and the time corresponding to the historical load power are normalized and data labeled to obtain training data; wherein different labels of the data labeling represent different multi-energy complementarity and collaborative optimization schemes;

[0015] Based on the training data, N multi-energy complementarity and collaborative optimization models are trained respectively to obtain the N pre-trained multi-energy complementarity and collaborative optimization models.

[0016] Preferably, the multi-energy complementarity and collaborative optimization model includes a CNN model, a FCNN model and a RNN model.

[0017] Preferably, the predicted load power and the historical load power are normalized based on the following formula:

[0018]

[0019] Among them, x norm represents the normalized predicted load power / represents the normalized historical load power; x represents the predicted load power to be normalized / represents the historical load power to be normalized; x min Indicates the minimum value of load power; x max Indicates the maximum value of the load power.

[0020] Preferably, the time corresponding to the predicted load power and the time corresponding to the historical load power are normalized using the following formula:

[0021]

[0022] Among them, y norm = represents the time corresponding to the normalized predicted load power / represents the time corresponding to the normalized historical load power; y = y h :y m :y s represents the time corresponding to the predicted load power to be normalized / represents the time corresponding to the historical load power to be normalized; y h ,y m ,y s Corresponding to hours, minutes, and seconds respectively.

[0023] Preferably, if the N multi-energy complementarity and collaborative optimization schemes are different, the final multi-energy complementarity and collaborative optimization scheme is obtained by using credibility, which specifically includes the following steps:

[0024] If the credibility of N multi-energy complementarity and collaborative optimization schemes is higher than or partially higher than the credibility threshold, the multi-energy complementarity and collaborative optimization scheme with the highest credibility will be used as the final multi-energy complementarity and collaborative optimization scheme;

[0025] If the credibility of the N multi-energy complementarity and collaborative optimization schemes is lower than the credibility threshold, the confidence of the N multi-energy complementarity and collaborative optimization schemes is calculated;

[0026] Calculate the weighted sum of N pairs of credibility and confidence; take the multi-energy complementarity and collaborative optimization scheme corresponding to the maximum weighted sum result as the final multi-energy complementarity and collaborative optimization scheme.

[0027] Preferably, the credibility threshold is 0.25.

[0028] Preferably, the weighted summation result is calculated based on the following formula:

[0029] z=λ1A ced +λ2A con ;

[0030] Among them, z represents the weighted summation result; A ced Indicates credibility; A con represents the confidence level; λ1 and λ2 represent the weighting coefficients.

[0031] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the multi-energy complementarity and collaborative optimization method of the microgrid when executing the computer program.

[0032] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for multi-energy complementarity and collaborative optimization of a microgrid, which can realize multi-energy complementarity and collaborative optimization of a microgrid in real time, quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0034] Figure 1 A flowchart of a multi-energy complementarity and collaborative optimization method for a microgrid provided in a certain embodiment of the present invention;

[0035] Figure 2A flow chart of a method for obtaining N pre-trained multi-energy complementarity and collaborative optimization models provided in a certain embodiment of the present invention;

[0036] Figure 3 An electronic device is provided in a certain embodiment of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] like Figure 1 As shown, the embodiment of the present invention discloses a multi-energy complementarity and collaborative optimization method for a microgrid, comprising the following steps:

[0039] Obtain the predicted load power of the microgrid and the time corresponding to the predicted load power;

[0040] The predicted load power and the time corresponding to the predicted load power are normalized and then input into N pre-trained multi-energy complementation and collaborative optimization models to obtain N multi-energy complementation and collaborative optimization solutions;

[0041] If the N multi-energy complementarity and collaborative optimization schemes are the same, then the multi-energy complementarity and collaborative optimization schemes are directly output;

[0042] If the N multi-energy complementarity and collaborative optimization schemes are different, the corresponding credibility of the N multi-energy complementarity and collaborative optimization schemes is obtained, and the final multi-energy complementarity and collaborative optimization scheme is obtained using the credibility.

[0043] In one embodiment, the microgrid includes solar photovoltaic, wind power generation, diesel power generation and energy storage system.

[0044] It is understandable that the microgrid may also include hydropower generation, etc., and the specific composition of the microgrid is determined based on the on-site geographical location and conditions.

[0045] In one embodiment, if Figure 2 As shown, N pre-trained multi-energy complementary and collaborative optimization models are obtained based on the following steps:

[0046] Obtain the historical load power of the microgrid and the time corresponding to the historical load power;

[0047] The historical load power and the time corresponding to the historical load power are normalized and labeled to obtain training data; different labels of the data labeling represent different multi-energy complementarity and collaborative optimization solutions;

[0048] It is understandable that data annotation is performed by experts in related fields to improve the reliability of training data.

[0049] Based on the training data, N multi-energy complementarity and collaborative optimization models are trained respectively to obtain N pre-trained multi-energy complementarity and collaborative optimization models.

[0050] In one embodiment, the multi-energy complementarity and collaborative optimization model includes a CNN model, a FCNN model and a RNN model.

[0051] It can be understood that the multi-energy complementarity and collaborative optimization model can also include variants of the CNN model, the FCNN model and the RNN model.

[0052] In one embodiment, the predicted load power and the historical load power are normalized based on the following formula:

[0053]

[0054] Among them, x norm represents the normalized predicted load power / represents the normalized historical load power; x represents the predicted load power to be normalized / represents the historical load power to be normalized; x min Indicates the minimum value of load power; x max Indicates the maximum value of the load power.

[0055] In one embodiment, the time corresponding to the predicted load power and the time corresponding to the historical load power are normalized using the following formula:

[0056]

[0057] Among them, y norm = represents the time corresponding to the normalized predicted load power / represents the time corresponding to the normalized historical load power; y = y h :y m :y s represents the time corresponding to the predicted load power to be normalized / represents the time corresponding to the historical load power to be normalized; y h ,y m ,y s Corresponding to hours, minutes, and seconds respectively.

[0058] It can be understood that: assuming that the time corresponding to the predicted (historical) load power to be normalized is 15:20:30 (i.e. 15:20:30); then the time corresponding to the normalized predicted (historical) load power is:

[0059] In a certain embodiment, if the N multi-energy complementary and collaborative optimization schemes are different, the final multi-energy complementary and collaborative optimization scheme is obtained by using the credibility, which specifically includes the following steps:

[0060] If the credibility of N multi-energy complementarity and collaborative optimization schemes is higher than or partially higher than the credibility threshold, the multi-energy complementarity and collaborative optimization scheme with the highest credibility will be used as the final multi-energy complementarity and collaborative optimization scheme;

[0061] If the credibility of N multi-energy complementarity and collaborative optimization schemes is lower than the credibility threshold, the confidence of the N multi-energy complementarity and collaborative optimization schemes is calculated;

[0062] Calculate the weighted sum of N pairs of credibility and confidence; take the multi-energy complementarity and collaborative optimization scheme corresponding to the maximum weighted sum result as the final multi-energy complementarity and collaborative optimization scheme.

[0063] In one embodiment, the confidence threshold is 0.25.

[0064] In one embodiment, the weighted summation result is calculated based on the following formula:

[0065] z=λ1A ced +λ2A con ;

[0066] Among them, z represents the weighted summation result; A ced Indicates credibility; A con represents the confidence level; λ1 and λ2 represent the weighting coefficients.

[0067] The embodiment of the present invention also discloses a physical structure diagram of an electronic device, such as Figure 3 As shown, the electronic device may include: a processor 301, a communications interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communications interface 302 and the memory 303 communicate with each other through the communication bus 304. The processor 301 may call the logic instructions in the memory 303 to execute the multi-energy complementarity and collaborative optimization method of the microgrid.

[0068] In addition, the logic instructions in the above-mentioned memory 303 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0069] On the other hand, an embodiment of the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-mentioned microgrid multi-energy complementarity and collaborative optimization method.

[0070] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0071] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-energy complementarity and collaborative optimization method for a microgrid, characterized in that: The following steps are involved: Obtaining the predicted load power of the microgrid and the time corresponding to the predicted load power; The predicted load power and the time corresponding to the predicted load power are normalized and then input into N pre-trained multi-energy complementation and collaborative optimization models to obtain N multi-energy complementation and collaborative optimization solutions; If the N multi-energy complementarity and collaborative optimization schemes are the same, then the multi-energy complementarity and collaborative optimization schemes are directly output; If the N multi-energy complementarity and collaborative optimization schemes are different, the corresponding credibility of the N multi-energy complementarity and collaborative optimization schemes is obtained, and the final multi-energy complementarity and collaborative optimization scheme is obtained using the credibility.

2. A multi-energy complementarity and collaborative optimization method for a microgrid according to claim 1, characterized in that: The microgrid includes solar photovoltaic, wind power generation, diesel power generation and energy storage system.

3. The multi-energy complementarity and collaborative optimization method of a microgrid according to claim 1 is characterized in that: N pre-trained multi-energy complementarity and collaborative optimization models are obtained based on the following steps: Obtain the historical load power of the microgrid and the time corresponding to the historical load power; The historical load power and the time corresponding to the historical load power are normalized and data labeled to obtain training data; wherein different labels of the data labeling represent different multi-energy complementarity and collaborative optimization schemes; Based on the training data, N multi-energy complementarity and collaborative optimization models are trained respectively to obtain the N pre-trained multi-energy complementarity and collaborative optimization models.

4. A multi-energy complementarity and collaborative optimization method for a microgrid according to claim 3, characterized in that: Multi-energy complementarity and collaborative optimization models include CNN model, FCNN model and RNN model.

5. The multi-energy complementarity and collaborative optimization method of a microgrid according to claim 3 is characterized in that: The predicted load power and historical load power are normalized based on the following formula: Among them, x norm represents the normalized predicted load power / represents the normalized historical load power; x represents the predicted load power to be normalized / represents the historical load power to be normalized; x min Indicates the minimum value of load power; x max Indicates the maximum value of the load power.

6. A multi-energy complementarity and collaborative optimization method for a microgrid according to claim 3, characterized in that: The time corresponding to the predicted load power and the time corresponding to the historical load power are normalized using the following formula: Among them, y norm = represents the time corresponding to the normalized predicted load power / represents the time corresponding to the normalized historical load power; y = y h :y m :y s represents the time corresponding to the predicted load power to be normalized / represents the time corresponding to the historical load power to be normalized; y h ,y m ,y s Corresponding to hours, minutes, and seconds respectively.

7. The multi-energy complementarity and collaborative optimization method of a microgrid according to claim 1 is characterized in that: If the N multi-energy complementary and collaborative optimization schemes are different, the final multi-energy complementary and collaborative optimization scheme is obtained by using the credibility, which specifically includes the following steps: If the credibility of N multi-energy complementarity and collaborative optimization schemes is higher than or partially higher than the credibility threshold, the multi-energy complementarity and collaborative optimization scheme with the highest credibility will be used as the final multi-energy complementarity and collaborative optimization scheme; If the credibility of the N multi-energy complementarity and collaborative optimization schemes is lower than the credibility threshold, the confidence of the N multi-energy complementarity and collaborative optimization schemes is calculated; Calculate the weighted sum of N pairs of credibility and confidence; take the multi-energy complementarity and collaborative optimization scheme corresponding to the maximum weighted sum result as the final multi-energy complementarity and collaborative optimization scheme.

8. A multi-energy complementarity and collaborative optimization method for a microgrid according to claim 7, characterized in that: The credibility threshold is 0.

25.

9. A multi-energy complementarity and collaborative optimization method for a microgrid according to claim 7, characterized in that: The weighted sum is calculated based on the following formula: z=λ1A ced +λ2A con ; Among them, z represents the weighted summation result; A ced Indicates credibility; A con represents the confidence level; λ1 and λ2 represent the weighting coefficients.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the multi-energy complementarity and collaborative optimization method of the microgrid as described in any one of claims 1 to 9 is implemented.

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