CN114066031A - Day-by-day optimization scheduling method and system of comprehensive energy system - Google Patents
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Abstract
The present disclosure provides a day-in optimal scheduling method and system for an integrated energy system, which comprises the steps of obtaining the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error; decomposing the source-to-charge error into a set of components at different frequencies; different energy storage devices are transferred according to the frequency of the components, and day-by-day optimal scheduling of the comprehensive energy system is realized; according to the method, the energy storage equipment is flexibly adjusted according to real-time errors of supply and demand, so that high source-load matching in a short period is realized, and efficient operation of the comprehensive energy system is guaranteed.
Description
Technical Field
The disclosure belongs to the technical field of energy scheduling, and particularly relates to a day-interior optimal scheduling method and system of an integrated energy system.
Background
With the development of society, the global energy and environment problems are increasingly prominent, and the severe consumption of primary energy and the emission of a large amount of carbon dioxide further aggravate the environmental deterioration; the vigorous development of new energy sources such as wind energy, photovoltaic energy, tidal energy and the like becomes a necessary measure for reducing the emission of carbon dioxide and ensuring the continuous supply of energy sources. The comprehensive energy system can improve the energy utilization rate by realizing the complementation and mutual assistance of various energy sources, flexibly meets the multiple energy requirements of users, and becomes an ideal way for global energy structure transformation at present.
However, the new energy has the characteristics of strong randomness and large fluctuation, and the optimal scheduling problem of the comprehensive energy system is increasingly complicated along with the access of high-proportion new energy; only simple day-ahead dispatch plans have failed to accommodate the operational needs of the system; aiming at the short-term source-load matching day-based optimized scheduling, due to the fact that the scheduling step length and the scheduling strategy are flexibly adjusted, the characteristics of randomness and strong volatility of new energy can be effectively responded, the consumption rate of the new energy is improved, and the safe, stable and efficient operation of the system is guaranteed.
At present, scholars at home and abroad have made corresponding research on day-ahead optimized scheduling of the comprehensive energy system, but the research on day-ahead optimized scheduling is less; the search shows that in the Chinese invention patent CN202011528062.1, namely an MPC and LODDLC based multi-time scale optimization scheduling method for an integrated energy system, an MPC frame is integrated into the optimization scheduling of the integrated energy system, a day-ahead-day-inside-real-time scheduling model is established, and the multi-time scale scheduling of the integrated energy system is realized; in the Chinese patent CN201910673308.5 'comprehensive energy system multi-time scale energy scheduling method considering multi-energy collaborative optimization', a day-ahead optimization result is corrected and solved based on a short-term load prediction result, rolling optimization is realized at the lowest cost, and the real-time output of equipment is determined; the methods effectively correct the error of the day-ahead optimal scheduling, thereby further optimizing the operation strategy of the system.
The inventor of the present disclosure finds that none of the above-mentioned optimal scheduling methods fully exerts the role of the energy storage link in the system by considering different characteristics of the energy storage device.
Disclosure of Invention
The invention aims to solve the problems and provides a day-interior optimization scheduling method and system of an integrated energy system.
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present disclosure provides a method for optimizing scheduling of an integrated energy system in a day, including:
acquiring the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error;
decomposing the source-to-charge error into a set of components at different frequencies;
and different energy storage devices are transferred according to the frequency of the components, so that day-by-day optimal scheduling of the comprehensive energy system is realized.
Further, the random matching degree refers to the similarity degree of the source-load curves.
Further, the random matching degree is related to the sum of squared residuals and the total mean value.
Furthermore, the residual square is obtained according to the load value and the equipment output, and the total mean value is obtained according to the load value and the load mean value.
Further, a set empirical mode decomposition method is adopted to decompose the source-load error into a group of components with different frequencies, and the method comprises the following steps:
adding normally distributed white noise into the source load error;
taking the signal added with the white noise as a whole, and then carrying out empirical mode decomposition to obtain each frequency component;
repeating the steps, and adding a new normal distribution white noise sequence each time;
and performing integrated average processing on each frequency component obtained each time to obtain a final result.
Furthermore, after each frequency component of the source-charge error is obtained, different characteristics of different energy storage devices are called, the high-frequency error component is filled by using a super capacitor, and the low-frequency component is called for battery processing.
Furthermore, in the running process of the energy storage device, the source-charge matching degree is continuously calculated, and the source-storage-charge random matching is realized.
In a second aspect, the present disclosure further provides a day-to-day optimization scheduling system of an integrated energy system, including an error calculation module, an error decomposition module and an optimization scheduling module;
the error calculation module configured to: acquiring the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error;
the error decomposition module configured to: decomposing the source-to-charge error into a set of components at different frequencies;
the optimized scheduling module configured to: and different energy storage devices are transferred according to the frequency of the components, so that day-by-day optimal scheduling of the comprehensive energy system is realized.
In a third aspect, the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, performs the steps of the method for in-day optimal scheduling of an integrated energy system according to the first aspect.
In a fourth aspect, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the method for day-to-day optimal scheduling of an integrated energy system according to the first aspect.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the energy storage equipment is flexibly adjusted according to real-time errors of supply and demand, so that high source-load matching in a short period is realized, and efficient operation of a comprehensive energy system is guaranteed;
2. the method and the device can fully play the characteristics of different energy storage links by flexibly moving the energy storage equipment, and realize the short-term high matching of source-storage-load.
Drawings
The accompanying drawings, which form a part hereof, are included to provide a further understanding of the present embodiments, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present embodiments and together with the description serve to explain the present embodiments without unduly limiting the present embodiments.
Fig. 1 is a flow chart of embodiment 1 of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1:
as shown in fig. 1, the embodiment provides a method for optimizing and scheduling an integrated energy system in the day, including:
defining short-term random matching degree of a supply side and a demand side, and acquiring a source-load error when the random matching degree reaches a set value;
decomposing the source-load error into a set of components at different frequencies by using an ensemble empirical mode decomposition method;
and different energy storage devices are flexibly adjusted according to the frequency of the components, more energy storage devices are added, less energy storage devices are added, and day-by-day optimal scheduling of the comprehensive energy system is realized.
In this embodiment, the random matching degree refers to the similarity of the source-load curves; the random matching degree is related to the sum of squares of the residual errors and the total mean value; the residual square is obtained according to the load value and the equipment output, and the total average value is obtained according to the load value and the load average value.
Specifically, the source-load random matching degree is calculated as:
aiming at the source-load error in a short period, a random matching degree concept is introduced, and the similarity degree of a source-load curve is defined as the source-load random matching degree. The definition of the random matching degree is as follows:
the sum of the squares of the residuals is:
the overall mean is:
the random matching degree is:
wherein SSresAs a sum of squared residuals, SStotIs the overall mean, M is the random match, yiIs the value of the load at the time i,
for the device force at time i,
is the load mean.
The value range of M is 0-1 as defined by a formula, and the closer the random matching degree is to 1, the closer the equipment output curve is to the required electricity of each load, and the better the fitting degree is. To achieve a short term source-to-load height match, the random degree of match criterion is set to 0.98. And when the random matching degree is higher than 0.98, continuing to operate the day-ahead optimization strategy, and when the random matching degree is lower than 0.98, operating the day-ahead optimization strategy of the next step.
In this embodiment, when the random matching degree is lower than 0.98, a further day-wide optimization strategy is started. And calculating the error between the source and the load, and decomposing the error into a group of components with different frequencies by using a set empirical mode decomposition method. The steps of the ensemble empirical mode decomposition are as follows:
(1) adding normally distributed white noise into the source load error;
(2) taking the signal added with the white noise as a whole, and then carrying out empirical mode decomposition to obtain each IMF component;
(4) repeating the steps 1 and 2, and adding a new normal distribution white noise sequence each time;
(4) and performing integrated average treatment on the IMF obtained each time to obtain a final result.
In this embodiment, after obtaining the IMF component of the source charge error, different characteristics of different energy storage devices are fully tuned. The battery has large capacity but slow charge and discharge, and the super capacitor has small capacity but rapid charge and discharge. Therefore, the high-frequency error component is filled by the super capacitor, and the low-frequency component invokes the battery for processing.
In this embodiment, in the operation process of the energy storage device, the source-charge matching degree is continuously calculated to meet the requirement of the random matching degree, so that the source-storage-charge random matching is realized.
Example 2:
the embodiment provides a day-interior optimal scheduling system of a comprehensive energy system, which comprises an error calculation module, an error decomposition module and an optimal scheduling module;
the error calculation module configured to: acquiring the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error;
the error decomposition module configured to: decomposing the source-to-charge error into a set of components at different frequencies;
the optimized scheduling module configured to: and different energy storage devices are transferred according to the frequency of the components, so that day-by-day optimal scheduling of the comprehensive energy system is realized.
Example 3:
the present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for in-day optimal scheduling of an integrated energy system according to embodiment 1.
Example 4:
the embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the steps of the method for optimizing and scheduling the integrated energy system in the day described in embodiment 1 are implemented.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and those skilled in the art can make various modifications and variations. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present embodiment should be included in the protection scope of the present embodiment.
Claims (10)
1. An in-day optimization scheduling method of an integrated energy system is characterized by comprising the following steps:
acquiring the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error;
decomposing the source-to-charge error into a set of components at different frequencies;
and different energy storage devices are transferred according to the frequency of the components, so that day-by-day optimal scheduling of the comprehensive energy system is realized.
2. The method according to claim 1, wherein the random matching degree is a similarity of source-load curves.
3. The method of claim 1, wherein the random matching metric is related to a sum of squared residuals and a total mean.
4. The method of claim 3, wherein the residual squares are derived from the load values and the plant outputs, and the total mean is derived from the load values and the load mean.
5. The method of claim 1, wherein the method of ensemble empirical mode decomposition is used to decompose the source-to-load error into a set of components of different frequencies, comprising the steps of:
adding normally distributed white noise into the source load error;
taking the signal added with the white noise as a whole, and then carrying out empirical mode decomposition to obtain each frequency component;
repeating the steps, and adding a new normal distribution white noise sequence each time;
and performing integrated average processing on each frequency component obtained each time to obtain a final result.
6. The in-day optimal scheduling method of an integrated energy system according to claim 1, wherein after obtaining each frequency component of the source-charge error, different characteristics of different energy storage devices are mobilized, the high frequency error component is filled by a super capacitor, and the low frequency component is mobilized for battery processing.
7. The in-day optimal scheduling method of the integrated energy system according to claim 1, wherein the source-charge matching degree is continuously calculated during the operation of the energy storage device, so as to realize the random matching of source-storage-charge.
8. The day-interior optimization scheduling system of the comprehensive energy system is characterized by comprising an error calculation module, an error decomposition module and an optimization scheduling module;
the error calculation module configured to: acquiring the random matching degree of a supply side and a demand side; when the random matching degree reaches a set value, acquiring a source-load error;
the error decomposition module configured to: decomposing the source-to-charge error into a set of components at different frequencies;
the optimized scheduling module configured to: and different energy storage devices are transferred according to the frequency of the components, so that day-by-day optimal scheduling of the comprehensive energy system is realized.
9. A computer-readable storage medium, on which a computer program is stored for fingerprint similarity calculation, wherein the program, when executed by a processor, implements the steps of the method for intraday optimal scheduling of an integrated energy system according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for in-day optimal scheduling of an integrated energy system according to any of claims 1-7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
| CN110676861A (en) * | 2019-09-11 | 2020-01-10 | 台州宏远电力设计院有限公司 | Capacity optimization configuration method for composite energy storage device of power distribution network |
| CN111030141A (en) * | 2019-12-29 | 2020-04-17 | 上海电力大学 | Source-load cooperative distributed optimization regulation and control method based on consistency algorithm |
| CN111355230A (en) * | 2018-12-24 | 2020-06-30 | 中国电力科学研究院有限公司 | Optimized scheduling method and system for comprehensive energy system |
| CN112700094A (en) * | 2020-12-22 | 2021-04-23 | 上海电力大学 | Multi-time scale optimization scheduling method of comprehensive energy system based on MPC and LODDLC |
| CN113239624A (en) * | 2021-05-21 | 2021-08-10 | 长沙理工大学 | Short-term load prediction method, equipment and medium based on neural network combination model |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
| CN111355230A (en) * | 2018-12-24 | 2020-06-30 | 中国电力科学研究院有限公司 | Optimized scheduling method and system for comprehensive energy system |
| CN110676861A (en) * | 2019-09-11 | 2020-01-10 | 台州宏远电力设计院有限公司 | Capacity optimization configuration method for composite energy storage device of power distribution network |
| CN111030141A (en) * | 2019-12-29 | 2020-04-17 | 上海电力大学 | Source-load cooperative distributed optimization regulation and control method based on consistency algorithm |
| CN112700094A (en) * | 2020-12-22 | 2021-04-23 | 上海电力大学 | Multi-time scale optimization scheduling method of comprehensive energy system based on MPC and LODDLC |
| CN113239624A (en) * | 2021-05-21 | 2021-08-10 | 长沙理工大学 | Short-term load prediction method, equipment and medium based on neural network combination model |
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