Fused salt energy storage system working method and device based on energy consumption end power prediction, equipment and medium

Abstract

The invention discloses a fused salt energy storage system working method and device based on energy consumption end power prediction, equipment and a medium, and the method comprises the steps: obtaining the energy consumption data of a target region in a historical time period, and carrying out the time period division of the historical time period through the meteorological data in the historical time period; obtaining the to-be-stored energy capacity of the energy consumption time period; obtaining energy consumption type distribution of the energy consumption time period; re-determining an energy storage operation strategy of the fused salt energy storage system in the energy consumption period according to the energy storage state of the fused salt energy storage system in the energy consumption period, the to-be-stored energy capacity in the energy consumption period, the energy storage states of other energy storage systems in the energy consumption period, the energy consumption type distribution in the energy consumption period and the meteorological characteristics in the energy consumption period; and determining an energy storage operation strategy of the fused salt energy storage system of the target area in the preset time period. The invention belongs to the field of energy storage system operation prediction. According to the method, the regional energy consumption type and the time period weather characteristics are combined, the operation strategy of the fused salt energy storage system can be optimized, and the peak regulation capacity is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage system operation prediction, and in particular to a molten salt energy storage system working method, device, equipment and medium based on energy consumption end power prediction. Background Art

[0002] Molten salt energy storage system is an efficient, large-scale energy storage technology, widely used in solar thermal power plants and other scenarios that require long-term energy storage. Its core principle is to use high-temperature molten salt (usually a mixture of sodium nitrate and potassium nitrate) as a thermal energy storage medium.

[0003] In some cities, the molten salt energy storage system is one of the important energy storage systems. However, how to make the molten salt energy storage system coordinate with other energy storage systems to regulate electricity consumption and how to make the regulation more reasonable are urgent issues to be solved. Summary of the invention

[0004] The present invention solves the technical problem of unreasonable coordinated regulation of energy storage systems in the prior art by providing a molten salt energy storage system working method, device, equipment and medium based on power prediction at the energy consumption end, and achieves the technical effect of reasonably regulating the operation of the molten salt energy storage system.

[0005] In a first aspect, the present invention provides a molten salt energy storage system operating method based on energy consumption end power prediction, the method comprising: Obtain energy consumption data of the target area in a historical time period, and divide the historical time period into several energy consumption periods according to the meteorological data in the historical time period, wherein the duration n of each energy consumption period is greater than 1 hour, and n is an integer, and the meteorological data includes rainfall data, light data, and wind data; For each energy consumption period, steps S121-S123 are executed, including: Step S121, obtaining the energy storage capacity to be stored in the energy consumption period according to the sum of energy consumption in the energy consumption period and the unit output of the target area in the energy consumption period; Step S122, dividing the target area of ​​the energy usage period according to the energy usage characteristics to obtain the energy usage type distribution of the energy usage period, the energy usage characteristics including single life energy, single business energy, single processing and manufacturing energy, life and business energy, life and processing and manufacturing energy, business and processing and manufacturing energy, and mixed business, life and processing and manufacturing energy; Step S123, re-determining the energy storage operation strategy of the molten salt energy storage system in the energy usage period according to the energy storage state of the molten salt energy storage system in the energy usage period, the energy storage capacity to be stored in the energy usage period, the energy storage state of the remaining energy storage systems in the energy usage period, the energy usage type distribution in the energy usage period, and the meteorological characteristics of the energy usage period; Determine the energy storage operation strategy of the molten salt energy storage system in the target area during the preset time period according to the energy storage operation strategy of the molten salt energy storage system in several energy consumption time periods.

[0006] Furthermore, divide the target area of this energy consumption time period according to the energy consumption characteristics to obtain the energy consumption type distribution of this energy consumption time period, including: Divide the target area of this energy consumption time period according to the energy consumption characteristics to obtain the sub-areas corresponding to each energy consumption characteristic; Determine the energy consumption of the sub-areas under each energy consumption characteristic during this energy consumption time period; According to the energy consumption of the sub-areas under several energy consumption characteristics during this energy consumption time period, determine the energy consumption proportion of several energy consumption characteristics during this energy consumption time period, and use the energy consumption proportion of this energy consumption time period as the energy consumption type distribution of this energy consumption time period.

[0007] Furthermore, according to the energy storage state of the molten salt energy storage system in this energy consumption time period, the energy storage capacity to be stored in this energy consumption time period, the energy storage state of the remaining energy storage systems in this energy consumption time period, the energy consumption type distribution of this energy consumption time period, and the meteorological characteristics of this energy consumption time period, re-determine the energy storage operation strategy of the molten salt energy storage system in this energy consumption time period, including: When > 0 and :

[0008] Among them, if is less than 0, no energy storage is performed. If is greater than 0, energy storage is performed; When > 0 and :

[0009] Among them, is the charge-discharge strategy of the molten salt energy storage system in the th energy consumption time period, is the energy storage capacity to be stored in the th energy consumption time period, is the energy storage state of the molten salt energy storage system in the th energy consumption time period, is the energy storage state of the remaining energy storage systems in the th energy consumption time period, is a preset constant. When the meteorological characteristic of this energy consumption time period is sunny, it is recorded as 0.9, and for other meteorological characteristics, it is recorded as 0.5; Let it be a preset constant for the energy consumption type distribution. When the sum of the energy consumption of single processing and manufacturing, life and processing and manufacturing, commercial and processing and manufacturing, and the mixed energy consumption of commercial, life and processing and manufacturing in the energy consumption type distribution accounts for more than the preset threshold in the energy consumption period, it is recorded as 0.3, otherwise it is recorded as 0.8; When is 0:

[0010] Among them, is the maximum capacity of the molten salt energy storage system.

[0011] Furthermore, the historical time period is divided into several energy consumption periods based on the meteorological data in the historical time period, including: The meteorological data in the historical time period is preprocessed, where the data preprocessing includes missing value processing, outlier processing and data standardization processing; Construct several typical meteorological features, which at least include: rainy day feature, sunny day feature, cloudy feature and snowy feature; Determine the preset number of clusters; Under the K-means algorithm, the historical time period is divided into several energy consumption periods according to several typical meteorological features and the preset number of clusters.

[0012] Furthermore, according to the energy storage operation strategies of the molten salt energy storage systems in several energy consumption periods, determine the energy storage operation strategies of the molten salt energy storage systems in the target area in the preset time period, including: Bind the energy storage operation strategy of the molten salt energy storage system in the energy consumption period, the meteorological features of this energy consumption period and the energy consumption type distribution of this energy consumption period into a data group, and a total of several data groups are obtained; Input several data groups into the neural network to be trained to train the neural network to be trained. When the preset training requirements are met, save the latest neural network parameters and obtain the target neural network, where the neural network to be trained is used to predict the energy storage operation strategy of the molten salt energy storage system; Obtain the predicted meteorological features and predicted energy consumption type distribution in the preset time period, and input them into the target neural network to obtain the energy storage operation strategy of the molten salt energy storage system in the preset time period.

[0013] Furthermore, according to the energy storage operation strategies of the molten salt energy storage systems in several energy consumption periods, determining the energy storage operation strategies of the molten salt energy storage systems in the target area in the preset time period also includes: Divide the preset time period into m preset time sub-segments, and the length of the preset time sub-segment is 1h; Sequentially match the predicted meteorological data of each preset time segment with the meteorological characteristics of several energy consumption periods, and screen out several target energy consumption periods. Among them, one preset time segment corresponds to several target energy consumption periods; Perform an energy consumption distribution similarity match based on the predicted energy consumption type distribution corresponding to the preset time segment and the energy consumption type distributions of several energy consumption periods to obtain the target energy consumption type distribution of the preset time segment; Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time segment according to the target energy consumption periods corresponding to each preset time segment and the corresponding target energy consumption type distribution; Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period according to the energy storage operation strategies of the molten salt energy storage systems corresponding to several preset time segments.

[0014] Furthermore, according to the sum of the energy consumptions of the energy consumption periods and the unit output of the target area during the energy consumption periods, obtain the energy storage capacity to be stored during the energy consumption periods, including:

[0015] Among them, is the energy storage capacity to be stored during the th energy consumption period, is the sum of the energy consumptions during the th energy consumption period, is the unit output during the th energy consumption period.

[0016] In a second aspect, the present invention provides a working device for a molten salt energy storage system based on end-use power prediction. The device includes: A data acquisition module, configured to acquire the energy consumption data of the target area within a historical time period, and divide the historical time period into several energy consumption periods based on the meteorological data within the historical time period. Among them, the duration n of each energy consumption period is greater than 1 h and n is an integer. The meteorological data includes rainfall data, sunlight data, and wind power data; The first strategy module is used to execute steps S121 - S123 for each energy consumption period, including: Step S121, obtaining the energy storage capacity to be stored in this energy consumption period according to the sum of energy consumption in the energy consumption period and the unit output of the target area in the energy consumption period; Step S122, dividing the target area in this energy consumption period with energy consumption characteristics to obtain the energy consumption type distribution in this energy consumption period, and the energy consumption characteristics include single residential energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, residential and commercial energy consumption, residential and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, residential and processing and manufacturing energy consumption; Step S123, re - determining the energy storage operation strategy of the molten salt energy storage system in this energy consumption period according to the energy storage state of the molten salt energy storage system in this energy consumption period, the energy storage capacity to be stored in this energy consumption period, the energy storage states of the other energy storage systems in this energy consumption period, the energy consumption type distribution in this energy consumption period, and the meteorological characteristics in this energy consumption period. The second strategy module is used to determine the energy storage operation strategy of the molten salt energy storage system in the target area in a preset time period according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods.

[0017] In a third aspect, the present invention provides an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute to implement the working method of the molten salt energy storage system based on the power prediction at the energy consumption end provided in the first aspect.

[0018] In a fourth aspect, the present invention provides a non - transitory computer - readable storage medium, when the instructions in the storage medium are executed by the processor of the electronic device, enabling the electronic device to execute and implement the working method of the molten salt energy storage system based on the power prediction at the energy consumption end provided in the first aspect.

[0019] One or more technical solutions provided in the present invention have at least the following technical effects or advantages: According to the periods divided by different weather characteristics, the present invention can identify the energy consumption laws under different weather patterns, and formulate more accurate charge - discharge strategies for the molten salt energy storage system in combination with the energy consumption laws under different weather patterns.

[0020] In the present invention, by re - determining the charge - discharge strategy of the molten salt energy storage system in the energy consumption period, it can provide more accurate guidance for the prediction of the charge - discharge strategy of the molten salt energy storage system.

[0021] By integrating the energy storage state, the capacity to be stored, and the collaborative data of multiple systems, the present invention can optimize the energy storage / energy release rhythm, make full use of the advantage of the large heat capacity of the molten salt system, optimize the heat storage efficiency during the grid low - valley period, and accurately match the high - grade energy consumption demands such as industrial heating during the peak period.

[0022] The present invention combines the types of energy consumption in the area and the meteorological characteristics of different time periods to achieve multi-energy coupled supply. By real-time monitoring the state of other energy storage systems, a complementary mechanism is established. When the short-term energy storage system is overloaded, the peak shaving ability of the molten salt energy storage is exerted.

[0023] The dynamic optimization process provided by the present invention can effectively solve the problems of waste heat storage or insufficient energy supply caused by traditional fixed strategies. Since the molten salt system has a slow thermal response, the present invention is based on multi-source data modeling and prediction, which can avoid the delay effect caused by thermal inertia. At the same time, through the coordinated scheduling of multiple systems, the renewable energy consumption capacity and overall economy are significantly improved.

[0024] The present invention can provide an accurate basis for predicting the energy storage operation strategy of the future molten salt energy storage system by splitting time periods and calculating the similarity based on meteorological and characteristic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flowchart of the working method of the molten salt energy storage system based on the power prediction at the energy consumption end provided by the present invention; Figure 2 It is a schematic structural diagram of the working device of the molten salt energy storage system based on the power prediction at the energy consumption end provided by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0027] The embodiments of the present invention provide a working method of a molten salt energy storage system based on the power prediction at the energy consumption end, which solves the technical problem of unreasonable coordinated regulation in the existing energy storage system.

[0028] The technical solution of the present invention to solve the above technical problem is generally as follows: Working method of molten salt energy storage system based on power prediction at the energy consumption end. The method includes: obtaining the energy consumption data of the target area within the historical time period, and dividing the historical time period according to the meteorological data within the historical time period to obtain a number of energy consumption time periods. Among them, the duration n of each energy consumption time period is greater than 1h and n is an integer. The meteorological data includes rainfall data, light data, and wind data; for each energy consumption time period, steps S121 - S123 are executed, including: step S121, obtaining the energy storage capacity to be stored in this energy consumption time period according to the sum of energy consumption in the energy consumption time period and the unit output of the target area in the energy consumption time period; step S122, dividing the target area of this energy consumption time period according to the energy consumption characteristics to obtain the energy consumption type distribution of this energy consumption time period. The energy consumption characteristics include single residential energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, residential and commercial energy consumption, residential and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, residential and processing and manufacturing energy consumption; step S123, re - determining the energy storage operation strategy of the molten salt energy storage system in this energy consumption time period according to the energy storage state of the molten salt energy storage system in this energy consumption time period, the energy storage capacity to be stored in this energy consumption time period, the energy storage state of the remaining energy storage systems in this energy consumption time period, the energy consumption type distribution of this energy consumption time period, and the meteorological characteristics of this energy consumption time period; according to the energy storage operation strategies of the molten salt energy storage system in a number of energy consumption time periods, determining the energy storage operation strategy of the molten salt energy storage system in the target area within the preset time period.

[0029] To better understand the above - mentioned technical solution, the above - mentioned technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0030] First, it should be noted that the term "and / or" appearing in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following associated objects.

[0031] The present invention provides a Figure 1 working method of molten salt energy storage system based on power prediction at the energy consumption end as shown, including steps S11 - S13: Step S11, obtaining the energy consumption data of the target area within the historical time period, and dividing the historical time period according to the meteorological data within the historical time period to obtain a number of energy consumption time periods. Among them, the duration n of each energy consumption time period is greater than 1h and n is an integer. The meteorological data includes rainfall data, light data, and wind data.

[0032] The target area can refer to an area with relatively complete supporting facilities and large scale. In the target area, its energy consumption can be obtained through multiple generator sets, such as thermal power generator sets, hydropower generator sets, wind power generator sets, and solar power generator sets, etc.

[0033] In the target area, there can be several types of energy storage facilities for regulating energy consumption, such as: battery energy storage systems, pumped-storage power stations, compressed air energy storage, and thermal energy storage systems (molten salt energy storage systems are a type of thermal energy storage system).

[0034] A molten salt energy storage system is an energy storage technology that uses molten salt as a heat storage medium. The molten salt energy storage system converts solar radiation into heat energy and uses molten salt to store this energy so that it can be converted into electrical energy when needed.

[0035] Divide the historical time period into several energy consumption time periods based on the meteorological data within the historical time period, including: Perform data preprocessing on the meteorological data within the historical time period, where the data preprocessing includes missing value processing, outlier processing, and data standardization processing.

[0036] The meteorological data of the historical time period can be the meteorological data of the target area in the past few years. The meteorological data can include temperature, humidity, air pressure, wind speed, wind direction, precipitation, cloud cover and cloud type, sunshine and radiation, visibility, extreme weather phenomena, and air quality, etc.

[0037] Construct several typical meteorological characteristics, which at least include: rainy day characteristics, sunny day characteristics, cloudy characteristics, and snowy characteristics.

[0038] Typical meteorological characteristics are characteristics representing different weather conditions.

[0039] For example: Rainy day characteristics: The average rainfall within a period or a day.

[0040] Sunny day characteristics: The cumulative number of hours of sufficient sunlight within a day or a period.

[0041] Snowy characteristics: The average snowfall within a day or a period.

[0042] Cloudy characteristics: The cloud cover within a day or a period.

[0043] In addition, combined characteristics can also be constructed, such as the sunshine index (combining light and rainfall), the extreme weather index (heavy rainfall, strong winds, etc.) to better capture complex weather patterns.

[0044] It can be understood that constructing several typical meteorological characteristics is not only to distinguish the weather of each time period, but also to combine each type of unit with weather characteristics. (The output efficiency of each type of unit is different under different weather conditions) Determine the preset number of clusters; under the K-means algorithm, divide the historical time period into several energy consumption periods according to several typical meteorological characteristics and the preset number of clusters.

[0045] K-means is a commonly used clustering algorithm. K-means assigns data points to k clusters through an iterative optimization method to minimize the within-cluster sum of squares. The elbow method or silhouette score can be used to evaluate the clustering effect under different k values, so as to select the best preset number of clusters.

[0046] Randomly select k data points as the initial centroids, calculate the distance from each data point to all centroids, and assign it to the nearest cluster; then recalculate the position of the centroid according to all points within the cluster. Repeat this process until the centroid no longer changes significantly.

[0047] After completing the clustering, the characteristics of each cluster (i.e., the typical meteorological characteristics represented by each cluster) can be analyzed, and based on this, the typical meteorological characteristics of each hour in the historical period can be defined, and the hours with continuous and the same typical meteorological characteristics are divided into one energy consumption period.

[0048] The time periods divided according to different weather characteristics in the present invention can identify the energy consumption laws under different weather patterns, and combine the energy consumption laws under different weather patterns to more accurately formulate charge and discharge strategies for the molten salt energy storage system.

[0049] Step S12, for each energy consumption period, execute steps S121 - S123.

[0050] Step S121, obtain the energy storage capacity to be stored in this energy consumption period according to the sum of energy consumption in the energy consumption period and the unit output of the target area in the energy consumption period.

[0051] Specifically, it includes:

[0052] Among them, is the energy storage capacity to be stored in the th energy consumption period, is the sum of energy consumption in the th energy consumption period, is the unit output of the th energy consumption period.

[0053] The sum of energy consumption in the energy consumption period refers to all energy consumption of the target area in this energy consumption period, and the unit output of the energy consumption period refers to the unit output of the power generation unit of the target area in this energy consumption period.

[0054] When When it is > 0, it indicates that energy storage by the energy storage system is required; when When it is < 0, it indicates that energy supply by the energy storage system is required; when When it is equal to 0, there is no energy storage or supply.

[0055] Step S122: Divide the target area in the energy consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution in the energy consumption period. The energy consumption characteristics include single household energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, household and commercial energy consumption, household and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, household and processing and manufacturing energy consumption. (Municipal energy consumption can be recorded as household energy consumption) The energy consumption characteristics refer to the energy consumption type of a certain area. For example, taking a floor A in a certain area as an example, A has several floors. When all floors of A are residential buildings, it indicates that the area occupied by A is single household energy consumption; if the first floor of A is commercial and the floors above the second floor are residential, then the area occupied by A is household and commercial energy consumption; if the first floor of A is commercial, the 2nd - 5th floors are production lines, and the floors above the 6th floor are living areas, then the area occupied by A is mixed commercial, household and processing and manufacturing energy consumption.

[0056] Divide the target area in the energy consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution in the energy consumption period, including: Divide the target area in the energy consumption period according to the energy consumption characteristics to obtain the sub - areas corresponding to each energy consumption characteristic.

[0057] According to the above example, divide the target area in turn to obtain several sub - areas.

[0058] Determine the energy consumption of the sub - areas under each energy consumption characteristic in the energy consumption period in turn; specifically, the energy consumption of the sub - areas with the same energy consumption characteristic in the energy consumption period can be summed up to obtain the energy consumption of the sub - areas under each energy consumption characteristic in the energy consumption period.

[0059] According to the energy consumption of the sub - areas under several energy consumption characteristics in the energy consumption period, determine the energy consumption proportion of several energy consumption characteristics in the energy consumption period, and take the energy consumption proportion in the energy consumption period as the energy consumption type distribution in the energy consumption period.

[0060] Compare the energy consumption of each energy consumption characteristic in a certain energy consumption period to obtain the energy consumption proportion.

[0061] For example, in a certain energy consumption period, the energy consumption proportions of single household energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, household and commercial energy consumption, household and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, household and processing and manufacturing energy consumption are = 0.15:0.08:0.21:0.34:0.03:0.17:0.02.

[0062] Step S123: Re-determine the energy storage operation strategy of the molten salt energy storage system for this energy consumption period according to the energy storage state of the molten salt energy storage system in this energy consumption period, the energy storage capacity to be stored in this energy consumption period, the energy storage states of the remaining energy storage systems in this energy consumption period, the energy consumption type distribution in this energy consumption period, and the meteorological characteristics of this energy consumption period.

[0063] It should be noted that the target area is a relatively mature city, which means that the supporting facilities for energy storage are relatively complete. For the molten salt energy storage system, the molten salt energy storage system has significant advantages in large-scale and long-term energy storage, especially in the field of solar thermal power generation. However, in terms of energy conversion efficiency and response speed (due to the existence of secondary conversion), it is lower compared to battery energy storage systems. Therefore, in the present invention, it is considered that the proportion of other energy storage systems set is much larger than that of the molten salt energy storage system, and when > 0, > .

[0064] Specifically, it includes: When > 0 and :

[0065] Among them, if is less than 0, no energy storage is performed; if is greater than 0, energy storage is performed; When > 0 and :

[0066] Among them, is the charge-discharge strategy of the molten salt energy storage system for the th energy consumption period, is the energy storage capacity to be stored in the th energy consumption period, is the energy storage state of the molten salt energy storage system for the th energy consumption period, is the energy storage state of the remaining energy storage systems for the th energy consumption period, is a preset constant, which is recorded as 0.9 when the meteorological characteristics of this energy consumption period are sunny, and recorded as 0.5 for other meteorological characteristics; is a preset constant for the energy consumption type distribution. When the sum of single processing and manufacturing energy consumption, living and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and commercial, living and processing and manufacturing mixed energy consumption in the energy consumption type distribution accounts for a proportion greater than the preset threshold in this energy consumption period, It is denoted as 0.3, otherwise it is denoted as 0.8; the preset threshold is 0.5, etc., which can be specifically determined according to the actual situation.

[0067] When is 0:

[0068] Among them, is the maximum capacity of the molten salt energy storage system.

[0069] The energy storage state of the molten salt energy storage system refers to the remaining energy storage capacity of the molten salt energy storage system, that is, the capacity that can still be used for energy storage. The energy storage state of the rest of the energy storage system refers to the remaining energy storage capacity of the rest of the energy storage system.

[0070] It should be particularly noted that the charge-discharge strategy of the molten salt energy storage system in the th energy consumption period does not refer to the charge-discharge strategy of the molten salt energy storage system in the th energy consumption period that has occurred in the past, but rather the charge-discharge strategy of the molten salt energy storage system in the th energy consumption period is re-determined.

[0071] By re-determining the charge-discharge strategy of the molten salt energy storage system in the th energy consumption period, the present invention can provide more accurate guidance for the prediction of the charge-discharge strategy of the molten salt energy storage system.

[0072] By integrating the energy storage state, the capacity to be stored, and the multi-system collaborative data, the present invention can optimize the energy storage / discharge rhythm, make full use of the advantage of the large heat capacity of the molten salt system, optimize the heat storage efficiency during the grid low period, and accurately match the high-grade energy consumption demands such as industrial heating during the peak period.

[0073] Combining the regional energy consumption type and the period characteristics, the present invention realizes the multi-energy coupling supply. By real-time monitoring the states of other energy storage systems and establishing a complementary mechanism, the peak-shaving ability of the molten salt energy storage can be exerted when the short-term energy storage system is overloaded.

[0074] The dynamic optimization process provided by the present invention can effectively solve the problems of heat storage waste or insufficient energy supply caused by traditional fixed strategies. Due to the slow thermal response of the molten salt system, the present invention is based on multi-source data modeling and prediction, which can avoid the delay effect caused by thermal inertia. At the same time, through multi-system collaborative scheduling, the renewable energy consumption capacity and the overall economy can be significantly improved.

[0075] Step S13: Determine the energy storage operation strategy of the molten salt energy storage system in the target area during the preset time period according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods.

[0076]

Method 1

[0077] The preset training requirements can be the maximum number of training times, the pass rate threshold, etc., which are not restricted here. The preset time period can be a certain future time period. The future time period should not be too far from the current time to facilitate the prediction of meteorological characteristics and the distribution of energy consumption types.

[0078]

Method 2

[0079] When the similarity matching between the predicted meteorological data and the meteorological characteristics of the energy consumption period is greater than the weather similarity threshold, the two can be matched.

[0080] Perform energy consumption distribution similarity matching according to the predicted energy consumption type distribution corresponding to the preset time sub-segment and the energy consumption type distributions of a number of energy consumption periods to obtain the target energy consumption type distribution of the preset time sub-segment.

[0081] Based on the above similarity processing method, when the predicted energy consumption type distribution corresponding to a certain preset time sub-segment and the energy consumption type distribution of a certain energy consumption period are greater than the preset energy consumption distribution similarity threshold, the two are matched. Similarly, the target energy consumption type distribution of this preset time sub-segment can include several.

[0082] Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time sub-segment according to the target energy consumption period corresponding to each preset time sub-segment and the corresponding target energy consumption type distribution; specifically, screen out the target energy consumption type distribution belonging to the preset time sub-segment from a number of target energy consumption periods, and take the highest value of the product of the similarities of the two as the energy storage operation strategy corresponding to the final energy consumption period.

[0083] For example, there are a total of 150 target energy consumption time periods. Among them, there are 50 types of energy consumption distributions belonging to this energy consumption time period. That is to say, among the 50 types, the highest value is determined by multiplying the similarity of meteorological characteristics by the similarity of energy consumption distribution, and the energy consumption time period corresponding to the highest value is used as the final energy consumption time period. The energy storage operation strategy of the molten salt energy storage system corresponding to the final energy consumption time period, that is, the energy storage operation strategy of the molten salt energy storage system corresponding to this preset time sub - segment.

[0084] Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period according to the energy storage operation strategies of the molten salt energy storage system corresponding to several preset time sub - segments.

[0085] By splitting time periods and calculating similarities based on meteorology and characteristics, the present invention can provide an accurate basis for predicting the energy storage operation strategy of future molten salt energy storage systems.

[0086] In summary, the present invention provides a working method for a molten salt energy storage system based on power prediction at the energy consumption end. The method includes: obtaining the energy consumption data of the target area in the historical time period, and dividing the historical time period into several energy consumption time periods based on the meteorological data in the historical time period, where the duration n of each energy consumption time period is greater than 1 h and n is an integer, and the meteorological data includes rainfall data, light data, and wind data; for each energy consumption time period, execute steps S121 - S123, including: step S121, obtain the energy storage capacity to be stored in this energy consumption time period according to the sum of energy consumption in the energy consumption time period and the unit output of the target area in the energy consumption time period; step S122, divide the target area of this energy consumption time period according to the energy consumption characteristics to obtain the energy consumption type distribution of this energy consumption time period, and the energy consumption characteristics include single - type domestic energy consumption, single - type commercial energy consumption, single - type processing and manufacturing energy consumption, domestic and commercial energy consumption, domestic and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, domestic and processing and manufacturing energy consumption; step S123, re - determine the energy storage operation strategy of the molten salt energy storage system in this energy consumption time period according to the energy storage state of the molten salt energy storage system in this energy consumption time period, the energy storage capacity to be stored in this energy consumption time period, the energy storage states of the remaining energy storage systems in this energy consumption time period, the energy consumption type distribution of this energy consumption time period, and the meteorological characteristics of this energy consumption time period; determine the energy storage operation strategy of the molten salt energy storage system in the preset time period of the target area according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption time periods.

[0087] The time periods divided according to different weather characteristics in the present invention can identify the energy consumption laws under different weather patterns, and formulate more accurate charge - discharge strategies for the molten salt energy storage system in combination with the energy consumption laws under different weather patterns.

[0088] The present invention re - determines the The charge-discharge strategy of the molten salt energy storage system in a specific energy consumption period can provide more accurate guidance for the prediction of the charge-discharge strategy of the molten salt energy storage system.

[0089] By integrating the energy storage state, the capacity to be stored, and the collaborative data of multiple systems, the present invention can optimize the heat storage / discharge rhythm, make full use of the advantage of the large heat capacity of the molten salt system, optimize the heat storage efficiency during the low grid period, and accurately match the high-grade energy consumption demands such as industrial heating during the peak period.

[0090] Combining the regional energy consumption type and the period characteristics, the present invention realizes multi-energy coupled supply. By real-time monitoring the state of other energy storage systems and establishing a complementary mechanism, the peak shaving ability of the molten salt energy storage can be exerted when the short-term energy storage system is overloaded.

[0091] The dynamic optimization process provided by the present invention can effectively solve the problems of heat storage waste or insufficient energy supply caused by traditional fixed strategies. Due to the slow thermal response of the molten salt system, the present invention is based on multi-source data modeling prediction, which can avoid the delay effect caused by thermal inertia. At the same time, through the collaborative scheduling of multiple systems, the renewable energy consumption capacity and overall economy are significantly improved.

[0092] By splitting the period and calculating the similarity based on meteorology and characteristics, the present invention can provide an accurate basis for the prediction of the energy storage operation strategy of the future molten salt energy storage system.

[0093] Based on the same inventive concept, the present invention provides a working device of a molten salt energy storage system based on the prediction of the power at the energy consumption end as shown in Figure 2 The device includes: A data acquisition module 21, configured to acquire the energy consumption data of the target area in the historical time period, and divide the historical time period based on the meteorological data in the historical time period to obtain a plurality of energy consumption periods. Wherein, the duration n of each energy consumption period is greater than 1 h and n is an integer, and the meteorological data includes rainfall data, illumination data, and wind power data; A first strategy module 22, configured to execute steps S121-S123 for each energy consumption period, including: step S121, obtaining the capacity to be stored in the energy consumption period according to the sum of the energy consumption in the energy consumption period and the unit output in the energy consumption period of the target area; step S122, dividing the target area in the energy consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution in the energy consumption period, and the energy consumption characteristics include single residential energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, residential and commercial energy consumption, residential and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, residential and processing and manufacturing energy consumption; step S123, re-determining the energy storage operation strategy of the molten salt energy storage system in the energy consumption period according to the energy storage state of the molten salt energy storage system in the energy consumption period, the capacity to be stored in the energy consumption period, the energy storage state of the remaining energy storage systems in the energy consumption period, the energy consumption type distribution in the energy consumption period, and the meteorological characteristics in the energy consumption period. The second strategy module 23 is configured to determine the energy storage operation strategy of the molten salt energy storage system in the target area within a preset time period according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods.

[0094] Based on the same inventive concept, the present application further provides an electronic device as shown, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute to implement the working method of the molten salt energy storage system based on the power prediction at the energy consumption end as provided above.

[0095] Based on the same inventive concept, the present application further provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute to implement the working method of the molten salt energy storage system based on the power prediction at the energy consumption end as provided above.

[0096] Since the electronic device introduced in this embodiment is the electronic device adopted for implementing the information processing method in the embodiments of the present invention, based on the information processing method introduced in the embodiments of the present invention, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present invention will not be described in detail here. As long as it is the electronic device adopted by those skilled in the art to implement the information processing method in the embodiments of the present invention, it falls within the scope of protection of the present invention.

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0098] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 this process or multiple processes and / or blocksFigure 1 means for the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one Figure 1 process or processes and / or boxes Figure 1 or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one Figure 1 process or processes and / or boxes Figure 1 or more boxes.

[0101] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0102] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A working method of a molten salt energy storage system based on power prediction at the energy consumption end, characterized in that, The method includes: Obtaining the energy consumption data of the target area in the historical time period, and dividing the historical time period based on the meteorological data in the historical time period to obtain a number of energy consumption time periods, where the duration n of each energy consumption time period is greater than 1h and n is an integer, and the meteorological data includes rainfall data, illumination data, and wind power data; For each energy consumption time period, steps S121 - S123 are executed, including: Step S121, obtaining the energy storage capacity to be stored in the energy consumption time period according to the sum of energy consumption in the energy consumption time period and the unit output of the target area in the energy consumption time period; Step S122, dividing the target area in the energy consumption time period with energy consumption characteristics to obtain the energy consumption type distribution in the energy consumption time period, and the energy consumption characteristics include single residential energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, residential and commercial energy consumption, residential and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, residential and processing and manufacturing energy consumption; Step S123, re - determining the energy storage operation strategy of the molten salt energy storage system in the energy consumption time period according to the energy storage state of the molten salt energy storage system in the energy consumption time period, the energy storage capacity to be stored in the energy consumption time period, the energy storage states of the remaining energy storage systems in the energy consumption time period, the energy consumption type distribution in the energy consumption time period, and the meteorological characteristics in the energy consumption time period; Determining the energy storage operation strategy of the molten salt energy storage system in the target area in the preset time period according to the energy storage operation strategies of the molten salt energy storage system in a number of energy consumption time periods.

2. The working method of the molten salt energy storage system based on the power prediction of the energy consumption end according to claim 1, characterized in that, Dividing the target area in the energy consumption time period with energy consumption characteristics to obtain the energy consumption type distribution in the energy consumption time period, including: Dividing the target area in the energy consumption time period with energy consumption characteristics to obtain sub - areas corresponding to each energy consumption characteristic; Determining the energy consumption of the sub - areas under each energy consumption characteristic in the energy consumption time period; Determining the energy consumption proportion of a number of energy consumption characteristics in the energy consumption time period according to the energy consumption of the sub - areas under a number of energy consumption characteristics in the energy consumption time period, and taking the energy consumption proportion in the energy consumption time period as the energy consumption type distribution in the energy consumption time period.

3. The working method of the molten salt energy storage system based on end - use power prediction according to claim 2, wherein, Re - determining the energy storage operation strategy of the molten salt energy storage system in the energy consumption time period according to the energy storage state of the molten salt energy storage system in the energy consumption time period, the energy storage capacity to be stored in the energy consumption time period, the energy storage states of the remaining energy storage systems in the energy consumption time period, the energy consumption type distribution in the energy consumption time period, and the meteorological characteristics in the energy consumption time period, including: When > 0 and : Among them, if is less than 0, energy storage is not performed. If is greater than 0, energy storage is performed; When > 0 and : Among them, is the charge-discharge strategy of the molten salt energy storage system for the th energy consumption period, is the energy storage capacity to be stored for the th energy consumption period, is the energy storage state of the molten salt energy storage system for the th energy consumption period, is the energy storage state of the remaining energy storage systems for the th energy consumption period, is a preset constant. When the meteorological characteristics of this energy consumption period are sunny, it is recorded as 0.9, and for other meteorological characteristics, it is recorded as 0.5; is a preset constant for the energy consumption type distribution. When the sum of the single processing and manufacturing energy consumption, the living and processing and manufacturing energy consumption, the commercial and processing and manufacturing energy consumption, and the commercial, living and processing and manufacturing mixed energy consumption in the energy consumption type distribution accounts for more than the preset threshold in this energy consumption period, it is recorded as 0.3, otherwise it is recorded as 0.8; When is 0: Among them, is the maximum capacity of the molten salt energy storage system.

4. The working method of the molten salt energy storage system based on the power prediction of the energy consumption end according to claim 1, characterized in that, Dividing the historical time period based on the meteorological data in the historical time period to obtain a number of energy consumption time periods, including: Performing data pre - processing on the meteorological data in the historical time period, where the data pre - processing includes missing value processing, outlier processing, and data standardization processing; Constructing a number of typical meteorological characteristics, including at least: rainy day characteristics, sunny day characteristics, cloudy characteristics, and snowy characteristics; Determining the preset number of clusters; Under the K - means algorithm, dividing the historical time period into a number of energy consumption time periods according to a number of typical meteorological characteristics and the preset number of clusters.

5. The operating method of the molten salt energy storage system based on the power prediction of the energy consumption end according to claim 1, characterized in that, Determining the energy storage operation strategy of the molten salt energy storage system in the target area in the preset time period according to the energy storage operation strategies of the molten salt energy storage system in a number of energy consumption time periods, including: Bind the energy storage operation strategy of the molten salt energy storage system during the energy consumption period, the meteorological characteristics during the energy consumption period, and the energy consumption type distribution during the energy consumption period into a data group, and a number of data groups are obtained in total; Input a number of data groups into the neural network to be trained to train the neural network to be trained. When the preset training requirements are met, save the latest neural network parameters and obtain the target neural network, where the neural network to be trained is used to predict the energy storage operation strategy of the molten salt energy storage system; Obtain the predicted meteorological characteristics and predicted energy consumption type distribution for a preset time period, and input them into the target neural network to obtain the energy storage operation strategy of the molten salt energy storage system for the preset time period.

6. The working method of the molten salt energy storage system based on end - use power prediction according to claim 1, wherein, Determine the energy storage operation strategy of the molten salt energy storage system in the target area for a preset time period according to the energy storage operation strategies of the molten salt energy storage system in a number of energy consumption periods, and further includes: Divide the preset time period into m preset time sub-segments, and the length of each preset time sub-segment is 1h; Perform meteorological similarity matching on the predicted meteorological data of each preset time sub-segment with the meteorological characteristics of a number of energy consumption periods in turn, and screen out a number of target energy consumption periods, where one preset time sub-segment corresponds to a number of target energy consumption periods; Perform energy consumption distribution similarity matching according to the predicted energy consumption type distribution corresponding to the preset time sub-segment and the energy consumption type distribution of a number of energy consumption periods to obtain the target energy consumption type distribution of the preset time sub-segment; Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time sub-segment according to the target energy consumption period corresponding to each preset time sub-segment and the corresponding target energy consumption type distribution; Determine the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period according to the energy storage operation strategies of the molten salt energy storage system corresponding to a number of preset time sub-segments.

7. The working method of the molten salt energy storage system based on end - use power prediction according to claim 1, characterized in that, Obtain the energy storage capacity to be stored during the energy consumption period according to the sum of the energy consumption during the energy consumption period and the unit output of the target area during the energy consumption period, and includes: Among them, is the energy storage capacity to be stored in the th energy consumption period, is the sum of energy consumption in the th energy consumption period, is the output of the unit in the th energy consumption period.

8. The working device of the molten salt energy storage system based on the power prediction of the energy consumption end, characterized in that, The device includes: A data acquisition module, configured to acquire the energy consumption data of the target area in the historical time period, and divide the historical time period by the meteorological data in the historical time period to obtain a number of energy consumption periods, where the duration n of each energy consumption period is greater than 1h and n is an integer, and the meteorological data includes rainfall data, light data, and wind data; The first strategy module is used to execute steps S121 - S123 for each energy consumption period, including: Step S121, obtaining the energy storage capacity to be stored in this energy consumption period according to the sum of energy consumption in the energy consumption period and the unit output of the target area in the energy consumption period; Step S122, dividing the target area in this energy consumption period with energy consumption characteristics to obtain the energy consumption type distribution in this energy consumption period, and the energy consumption characteristics include single household energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, household and commercial energy consumption, household and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, household and processing and manufacturing energy consumption; Step S123, re - determining the energy storage operation strategy of the molten salt energy storage system in this energy consumption period according to the energy storage state of the molten salt energy storage system in this energy consumption period, the energy storage capacity to be stored in this energy consumption period, the energy storage states of the remaining energy storage systems in this energy consumption period, the energy consumption type distribution in this energy consumption period, and the meteorological characteristics in this energy consumption period. The second strategy module is used to determine the energy storage operation strategy of the molten salt energy storage system in the target area in a preset time period according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods.

9. An electronic device, characterized in that, Including: A processor; A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute to implement the working method of the molten salt energy storage system based on power prediction at the energy consumption end as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device can execute to implement the working method of the molten salt energy storage system based on power prediction at the energy consumption end as described in any one of claims 1 to 7.

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