A method and system for optimizing the daily scheduling of an integrated energy system

Abstract

The present disclosure provides a method and system for optimizing the scheduling of an integrated energy system within a day, including: obtaining the random matching degree between the supply side and the demand side; obtaining the source-load error when the random matching degree reaches a set value; decomposing the source-load error into a group of components at different frequencies; mobilizing different energy storage devices according to the frequencies of the components to achieve the optimal scheduling of the integrated energy system within a day; the present disclosure flexibly mobilizes the energy storage devices according to the real-time error between supply and demand, thereby achieving a high degree of matching between the source and load in the short term, so as to ensure the efficient operation of the integrated energy system.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of energy scheduling, and in particular, relates to a method and system for optimizing the scheduling of an integrated energy system within a day. Background Art

[0002] With the development of society, global energy and environmental issues are becoming increasingly prominent. The serious consumption of primary energy and the emission of large amounts of carbon dioxide have further aggravated the deterioration of the environment. Vigorously developing new energy sources such as wind energy, photovoltaics and tidal energy has become a necessary measure to reduce carbon dioxide emissions and ensure the continuous supply of energy. Among them, the integrated energy system can achieve the mutual complementarity of multiple energy sources, improve energy utilization, and flexibly meet the multiple energy needs of users. It has become an ideal way to transform the global energy structure today.

[0003] However, new energy itself has the characteristics of strong randomness and volatility. With the access of a high proportion of new energy, the optimization and scheduling problem of the integrated energy system has become increasingly complex. Simple day-ahead scheduling plans alone can no longer meet the operating needs of the system. Intraday optimization scheduling for short-term source-load matching can effectively cope with the randomness and volatility of new energy due to its short scheduling step and flexible scheduling strategy adjustment, so as to improve the new energy absorption rate and ensure the safe, stable and efficient operation of the system.

[0004] At present, scholars at home and abroad have conducted corresponding research on the day-ahead optimal scheduling of integrated energy systems, but there are fewer studies on intraday optimal scheduling; the search found that in the Chinese invention patent CN202011528062.1 "A multi-time scale optimal scheduling method for an integrated energy system based on MPC and LODDLC", the MPC framework is integrated into the optimal scheduling of the integrated energy system, and a day-ahead-intraday-real-time scheduling model is established to realize the multi-time scale scheduling of the integrated energy system; in the Chinese invention patent CN201910673308.5 "Multi-time scale energy scheduling method for an integrated energy system considering multi-energy collaborative optimization", based on the short-term load forecast results, the day-ahead optimization results are corrected and solved, and the real-time output of the equipment is determined to achieve rolling optimization at the lowest cost; these methods have effectively corrected the errors of the day-ahead optimal scheduling, thereby further optimizing the system's operation strategy.

[0005] The inventors of the present disclosure have discovered that none of the above-mentioned optimization scheduling methods fully utilizes the role of the energy storage link in the system by considering the different characteristics of the energy storage devices. Summary of the invention

[0006] In order to solve the above problems, the present disclosure proposes a method and system for optimizing the daily scheduling of an integrated energy system. The present disclosure flexibly mobilizes energy storage equipment according to the real-time error between supply and demand, thereby achieving a high degree of source-load matching in the short term to ensure the efficient operation of the integrated energy system.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] In a first aspect, the present disclosure provides a method for optimizing the daily scheduling of a comprehensive energy system, comprising:

[0009] Obtain the random matching degree between the supply side and the demand side; when the random matching degree reaches the set value, obtain the source-load error;

[0010] Decompose the source-charge error into a set of components at different frequencies;

[0011] Different energy storage devices are mobilized according to the frequency of the components to achieve intraday optimal scheduling of the integrated energy system.

[0012] Furthermore, the random matching degree refers to the similarity of the source-charge curves.

[0013] Furthermore, the random matching degree is related to the residual sum of squares and the total mean.

[0014] Furthermore, the square of the residual is obtained based on the load value and the equipment output, and the total mean is obtained based on the load value and the load mean.

[0015] Furthermore, the source-load error is decomposed into a set of components with different frequencies using the ensemble empirical mode decomposition method, including the following steps:

[0016] Add normally distributed white noise to the source-charge error;

[0017] The signal with white noise added is taken as a whole, and then empirical mode decomposition is performed to obtain each frequency component;

[0018] Repeat the above steps, adding a new normally distributed white noise sequence each time;

[0019] The frequency components obtained each time are integrated and averaged as the final result.

[0020] Furthermore, after obtaining the frequency components of the source-load error, the different characteristics of different energy storage devices are mobilized to fill the high-frequency error components with supercapacitors and the low-frequency components are processed by batteries.

[0021] Furthermore, during the operation of the energy storage device, the source-load matching degree is continuously calculated to achieve random matching of source-storage-load.

[0022] In a second aspect, the present disclosure further provides an intraday optimization scheduling system for a comprehensive energy system, including an error calculation module, an error decomposition module and an optimization scheduling module;

[0023] The error calculation module is configured to: obtain the random matching degree between the supply side and the demand side; when the random matching degree reaches a set value, obtain the source-load error;

[0024] The error decomposition module is configured to: decompose the source-load error into a set of components at different frequencies;

[0025] The optimization scheduling module is configured to mobilize different energy storage devices according to the frequency of the components to achieve daily optimization scheduling of the integrated energy system.

[0026] In a third aspect, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the intraday optimization scheduling method for the integrated energy system described in the first aspect.

[0027] In a fourth aspect, the present disclosure further provides 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 program, the steps of the intraday optimization scheduling method for the integrated energy system described in the first aspect are implemented.

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

[0029] 1. The present invention flexibly mobilizes energy storage equipment according to the real-time error between supply and demand, thereby achieving a high degree of source-load matching in the short term to ensure the efficient operation of the integrated energy system;

[0030] 2. The present disclosure flexibly mobilizes energy storage equipment to give full play to the characteristics of different energy storage links and achieves a short-term high matching of source, storage and load. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0032] Figure 1 This is a flowchart of embodiment 1 of the present disclosure. DETAILED DESCRIPTION

[0033] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0034] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. 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 application belongs.

[0035] Embodiment 1:

[0036] like Figure 1 As shown, this embodiment provides a method for optimizing the daily scheduling of a comprehensive energy system, including:

[0037] Define the short-term random matching degree between the supply side and the demand side. When the random matching degree reaches the set value, obtain the source-load error.

[0038] The source-load error is decomposed into a set of components at different frequencies using the ensemble empirical mode decomposition method.

[0039] Different energy storage devices can be flexibly mobilized according to the frequency of the components, with more charging and less replenishment, to achieve intraday optimal scheduling of the integrated energy system.

[0040] In this embodiment, the random matching degree refers to the similarity of the source-load curve; the random matching degree is related to the residual square sum and the total mean; the residual square is obtained according to the load value and the equipment output, and the total mean is obtained according to the load value and the load average.

[0041] Specifically, the source-load random matching degree is calculated as:

[0042] In view of the short-term source-charge error, the concept of random matching is introduced, and the similarity of the source-charge curve is defined as the source-charge random matching. The definition of random matching is as follows:

[0043] The residual sum of squares is:

[0044]

[0045] The overall mean is:

[0046]

[0047] The random matching degree is:

[0048]

[0049] Among them, SS res is the residual sum of squares, SS tot is the total mean, M is the random matching degree, y i is the load value at time i, Contribute to the i-moment equipment, is the load mean.

[0050] From the formula definition, we can know that the value range of M is 0 to 1. When the random matching degree is closer to 1, it indicates that the equipment output curve is closer to the power demand of each load, and the degree of fit is better. In order to achieve short-term high matching of source and load, the standard value of random matching degree is set to 0.98. When the random matching degree is higher than 0.98, the day-ahead optimization strategy will continue to run. When the random matching degree is lower than 0.98, the next day-ahead optimization strategy will run.

[0051] In this embodiment, when the random matching degree is lower than 0.98, the next intraday optimization strategy is initiated. The error between the source and the load is calculated, and the error is decomposed into a set of components with different frequencies using the ensemble empirical mode decomposition method. The steps of the ensemble empirical mode decomposition are as follows:

[0052] (1) Add normally distributed white noise to the source-charge error;

[0053] (2) Treat the signal with white noise as a whole, and then perform empirical mode decomposition to obtain the IMF components;

[0054] (4) Repeat steps 1 and 2, adding a new normally distributed white noise sequence each time;

[0055] (4) The IMF obtained each time is integrated and averaged as the final result.

[0056] In this embodiment, after obtaining the IMF component of the source-load error, the different characteristics of different energy storage devices are fully mobilized. The battery has a large capacity but slow charging and discharging, while the supercapacitor has a small capacity but fast charging and discharging. Therefore, the high-frequency error component is filled by the supercapacitor, and the low-frequency component is processed by the battery.

[0057] In this embodiment, during the operation of the energy storage device, the source-load matching degree is continuously calculated to meet the random matching degree requirement, thereby realizing the random matching of source-storage-load.

[0058] Embodiment 2:

[0059] This embodiment provides an intraday optimization scheduling system for a comprehensive energy system, including an error calculation module, an error decomposition module and an optimization scheduling module;

[0060] The error calculation module is configured to: obtain the random matching degree between the supply side and the demand side; when the random matching degree reaches a set value, obtain the source-load error;

[0061] The error decomposition module is configured to: decompose the source-load error into a set of components at different frequencies;

[0062] The optimization scheduling module is configured to mobilize different energy storage devices according to the frequency of the components to achieve daily optimization scheduling of the integrated energy system.

[0063] Embodiment 3:

[0064] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the intraday optimization scheduling method for the integrated energy system described in Example 1 are implemented.

[0065] Embodiment 4:

[0066] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the intraday optimization scheduling method for the integrated energy system described in Example 1 are implemented.

[0067] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A method for optimizing the daily scheduling of a comprehensive energy system, characterized in that: include: Obtain the random matching degree between the supply side and the demand side; when the random matching degree reaches the set value, obtain the source-load error; Decompose the source-charge error into a set of components at different frequencies; Mobilize different energy storage devices according to the frequency of components to achieve intraday optimal scheduling of the integrated energy system; The ensemble empirical mode decomposition method is used to decompose the source-load error into a set of components with different frequencies, including the following steps: Add normally distributed white noise to the source-charge error; The signal with white noise added is taken as a whole, and then empirical mode decomposition is performed to obtain each frequency component; Repeat the above steps, adding a new normally distributed white noise sequence each time; The frequency components obtained each time are integrated and averaged as the final result.

2. The intraday optimization scheduling method of a comprehensive energy system according to claim 1, characterized in that: The random matching degree refers to the similarity of the source-charge curves.

3. The intraday optimization scheduling method of a comprehensive energy system according to claim 1, characterized in that: The random matching degree is related to the residual sum of squares and the overall mean.

4. The intraday optimization scheduling method of a comprehensive energy system according to claim 3, characterized in that: The square of the residual is obtained based on the load value and the equipment output, and the total mean is obtained based on the load value and the load average.

5. The intraday optimization scheduling method of a comprehensive energy system according to claim 1, characterized in that: After obtaining the frequency components of the source-load error, the different characteristics of different energy storage devices are mobilized to fill the high-frequency error components with supercapacitors and the low-frequency components are processed by batteries.

6. The intraday optimization scheduling method of a comprehensive energy system according to claim 1, characterized in that: During the operation of the energy storage equipment, the source-load matching degree is continuously calculated to achieve random matching of source, storage and load.

7. An intraday optimization scheduling system for an integrated energy system, using an intraday optimization scheduling method for an integrated energy system as claimed in claim 1, characterized in that: It includes error calculation module, error decomposition module and optimization scheduling module; The error calculation module is configured to: obtain the random matching degree between the supply side and the demand side; when the random matching degree reaches a set value, obtain the source-load error; The error decomposition module is configured to: decompose the source-load error into a set of components at different frequencies; The optimization scheduling module is configured to mobilize different energy storage devices according to the frequency of the components to achieve daily optimization scheduling of the integrated energy system.

8. A computer-readable storage medium having a computer program stored thereon for calculating fingerprint similarity, characterized in that: When the program is executed by a processor, the steps of the intraday optimization scheduling method for the integrated energy system as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the intraday optimization scheduling method for the integrated energy system as described in any one of claims 1-6 are implemented.

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