Working method, device, equipment and medium of molten salt energy storage system based on energy end power prediction

Abstract

The present invention discloses a working method, device, equipment and medium for a molten salt energy storage system based on power prediction at the energy consumption end, including: obtaining energy consumption data of a target area in a historical time period, and dividing the historical time period into time periods according to meteorological data in the historical time period; obtaining the energy storage capacity to be stored in the energy consumption time period; obtaining the energy consumption type distribution in the energy consumption time period; 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 status 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 status 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 of the energy consumption time period; determining the energy storage operation strategy of the molten salt energy storage system in the target area in a preset time period. The present invention belongs to the field of energy storage system operation prediction. The present invention can optimize the operation strategy of the molten salt energy storage system and improve the peak-shaving capability by combining regional energy consumption types and time period weather characteristics.

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 operating method, device, equipment and medium based on energy-consuming end power prediction. Background Art

[0002] Molten salt energy storage systems are a highly efficient, large-scale energy storage technology widely used in solar thermal power plants and other scenarios requiring long-term energy storage. Their core principle is to use high-temperature molten salt (typically 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, but 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 problems that need to be solved urgently. 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 energy-consuming end power prediction, 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 method for operating a molten salt energy storage system based on energy consumption end power prediction, the method comprising:

[0006] Obtain energy consumption data for the target area during a historical time period, and divide the historical time period into several energy consumption periods based on the meteorological data during the historical time period. The duration n of each energy consumption period is greater than 1 hour, and n is an integer. The meteorological data includes rainfall data, sunlight data, and wind data.

[0007] For each energy consumption period, steps S121-S123 are executed, including:

[0008] 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;

[0009] Step S122: Divide the target area of the energy usage period by energy usage characteristics to obtain the energy usage type distribution of the energy usage period. The energy usage characteristics include single residential energy usage, single commercial energy usage, single manufacturing energy usage, residential and commercial energy usage, residential and manufacturing energy usage, commercial and manufacturing energy usage, and mixed commercial, residential, and manufacturing energy usage.

[0010] Step S123, re-determining the energy storage operation strategy of the molten salt energy storage system for the energy usage period based on the energy storage status of the molten salt energy storage system for the energy usage period, the energy storage capacity to be stored for the energy usage period, the energy storage status of the remaining energy storage systems for the energy usage period, the energy consumption type distribution for the energy usage period, and the meteorological characteristics for the energy usage period;

[0011] According to the energy storage operation strategy of the molten salt energy storage system in several energy consumption periods, the energy storage operation strategy of the molten salt energy storage system in the target area in the preset time period is determined.

[0012] Furthermore, the target area of the energy usage period is divided according to the energy usage characteristics to obtain the energy usage type distribution of the energy usage period, including:

[0013] Divide the target area of the energy usage period by energy usage characteristics to obtain sub-areas corresponding to each energy usage characteristic;

[0014] Determine the energy consumption of the sub-area under each energy consumption characteristic during the energy consumption period;

[0015] According to the energy consumption of the sub-areas under several energy consumption characteristics in the energy consumption period, the energy consumption proportions of several energy consumption characteristics in the energy consumption period are determined, and the energy consumption proportions of the energy consumption period are used as the energy consumption type distribution of the energy consumption period.

[0016] Furthermore, based on the energy storage status of the molten salt energy storage system during the energy usage period, the energy storage capacity to be stored during the energy usage period, the energy storage status of the remaining energy storage systems during the energy usage period, the energy consumption type distribution during the energy usage period, and the meteorological characteristics during the energy usage period, the energy storage operation strategy of the molten salt energy storage system during the energy usage period is re-determined, including:

[0017] when >0 and hour:

[0018]

[0019] Among them, if If it is less than 0, no energy storage is performed. If it is greater than 0, energy storage is performed;

[0020] when >0 and hour:

[0021]

[0022] in, For the The charging and discharging strategy of the molten salt energy storage system in each energy consumption period, For the The energy storage capacity for each energy consumption period, For the The energy storage status of the molten salt energy storage system in each energy consumption period, For the The energy storage status of the remaining energy storage systems in each energy consumption period, is a preset constant. When the weather characteristic of the energy consumption period is sunny, it is recorded as 0.9, and other weather characteristics are recorded as 0.5; A constant is preset for the energy consumption type distribution. When the energy consumption proportion of single processing and manufacturing energy, life and processing and manufacturing energy, commercial and processing and manufacturing energy, and commercial, life and processing and manufacturing mixed energy in the energy consumption type distribution in the energy consumption period is greater than the preset threshold, Recorded as 0.3, otherwise recorded as 0.8;

[0023] when 0:00

[0024]

[0025] in, is the maximum capacity of the molten salt energy storage system.

[0026] Furthermore, the historical time period is divided into several energy consumption periods based on the meteorological data within the historical time period, including:

[0027] Perform data preprocessing on meteorological data within the historical time period, where data preprocessing includes missing value processing, outlier processing and data standardization;

[0028] Constructing several typical meteorological characteristics, including at least: rainy day characteristics, sunny day characteristics, cloudy day characteristics, and snowy day characteristics;

[0029] Determine the preset number of clusters;

[0030] Under the K-means algorithm, the historical time period is divided into several energy consumption periods according to several typical meteorological characteristics and the preset number of clusters.

[0031] Furthermore, based on the energy storage operation strategies of the molten salt energy storage system in the plurality of energy usage periods, the energy storage operation strategy of the molten salt energy storage system in the target area in the preset time period is determined, including:

[0032] The energy storage operation strategy of the molten salt energy storage system during the energy consumption period, the meteorological characteristics of the energy consumption period, and the energy consumption type distribution during the energy consumption period are bound into a data group, thereby obtaining a total of several data groups;

[0033] Inputting several data sets into a neural network to be trained to train the neural network, saving the latest neural network parameters when preset training requirements are met, and obtaining a target neural network, wherein the neural network to be trained is used to predict the energy storage operation strategy of the molten salt energy storage system;

[0034] The predicted meteorological characteristics and predicted energy consumption type distribution for the preset time period are obtained and input into the target neural network to obtain the energy storage operation strategy of the molten salt energy storage system for the preset time period.

[0035] Furthermore, determining the energy storage operation strategy of the molten salt energy storage system in the target area during the preset time period based on the energy storage operation strategy of the molten salt energy storage system during the plurality of energy usage time periods further includes:

[0036] Divide the preset time period into m preset time sub-segments, and the length of the preset time sub-segments is 1 hour;

[0037] The predicted meteorological data of each preset time sub-segment is sequentially matched with the meteorological characteristics of several energy consumption periods for meteorological similarity, and several target energy consumption periods are selected, wherein one preset time sub-segment corresponds to several target energy consumption periods;

[0038] Performing energy distribution similarity matching based on the predicted energy consumption type distribution corresponding to the preset time sub-segment and the energy consumption type distribution of several energy consumption periods to obtain the target energy consumption type distribution for the preset time sub-segment;

[0039] Determine the energy storage operation strategy of the molten salt energy storage system corresponding to each preset time subsegment based on the target energy consumption period corresponding to each preset time subsegment and the corresponding target energy consumption type distribution;

[0040] According to the energy storage operation strategies of the molten salt energy storage system corresponding to the plurality of preset time subsegments, the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period is determined.

[0041] Furthermore, based on the sum of energy consumption during the energy consumption period and the unit output of the target area during the energy consumption period, the energy storage capacity to be stored during the energy consumption period is obtained, including:

[0042]

[0043] in, For the The energy storage capacity for each energy consumption period, For the The sum of energy consumption during each energy consumption period, For the The unit output during each energy consumption period.

[0044] In a second aspect, the present invention provides a molten salt energy storage system operating device based on energy consumption end power prediction, the device comprising:

[0045] A data acquisition module is used to obtain energy consumption data of a target area within a historical time period, and divide the historical time period into several energy consumption periods according to the meteorological data within 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, sunlight data, and wind data;

[0046] 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 the energy consumption period according to the sum of the 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 consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution of the energy consumption period, the energy consumption characteristics including single living energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, living and commercial 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; 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 status of the molten salt energy storage system in the energy consumption period, the energy storage capacity to be stored in the energy consumption period, the energy storage status 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 of the energy consumption period;

[0047] The second strategy module is used to determine the energy storage operation strategy of the molten salt energy storage system in the target area during a preset time period based on the energy storage operation strategy of the molten salt energy storage system in several energy consumption time periods.

[0048] In a third aspect, the present invention provides an electronic device, comprising:

[0049] processor;

[0050] a memory for storing processor-executable instructions;

[0051] Among them, the processor is configured to execute to implement the molten salt energy storage system working method based on energy-consuming end power prediction as provided in the first aspect.

[0052] In a fourth aspect, the present invention provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the operating method of the molten salt energy storage system based on energy consumption end power prediction as provided in the first aspect.

[0053] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0054] The present invention divides time periods according to different weather characteristics, can identify energy consumption patterns under different weather modes, and combine the energy consumption patterns under different weather modes to more accurately formulate charging and discharging strategies for the molten salt energy storage system.

[0055] The present invention redefines The charging and discharging strategy of the molten salt energy storage system in each energy consumption period can provide more accurate guidance for the prediction of the charging and discharging strategy of the molten salt energy storage system.

[0056] By integrating energy storage status, storage capacity and multi-system collaborative data, the present invention can optimize the storage / release rhythm, fully utilize the large heat capacity of the molten salt system, optimize the heat storage efficiency during the grid off-peak period, and accurately match high-grade energy demand such as industrial heating during peak periods.

[0057] The present invention combines regional energy consumption types and time period meteorological characteristics to achieve multi-energy coupling supply. By real-time monitoring of the status of other energy storage systems, a complementary mechanism is established to give full play to the peak-shaving capacity of molten salt energy storage when the short-term energy storage system is overloaded.

[0058] The dynamic optimization process provided by this invention effectively addresses the problems of wasted heat storage or insufficient energy supply caused by traditional fixed strategies. Due to the slow thermal response of molten salt systems, this invention, based on multi-source data modeling and prediction, can mitigate the delays caused by thermal inertia. Furthermore, through multi-system coordinated scheduling, it significantly improves renewable energy absorption capacity and overall economic efficiency.

[0059] By splitting the time periods and performing similarity calculations based on meteorological conditions and characteristics, the present invention can provide an accurate basis for predicting the energy storage operation strategy of future molten salt energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A schematic flow chart of the working method of the molten salt energy storage system based on energy consumption end power prediction provided by the present invention;

[0062] Figure 2 This is a schematic diagram of the structure of the working device of the molten salt energy storage system based on energy-consuming end power prediction provided by the present invention. DETAILED DESCRIPTION

[0063] The embodiment of 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 operating method based on energy-consuming end power prediction.

[0064] The technical solution of the present invention is to solve the above technical problems, and the overall idea is as follows:

[0065] A working method of a molten salt energy storage system based on power prediction at the energy consumption end includes: obtaining energy consumption data of a target area in a historical time period, and dividing the historical time period into time periods according to meteorological data in the historical time period to obtain a number of energy consumption time periods, wherein 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, light data, and wind data; for each energy consumption time period, executing steps S121-S123, including: step S121, 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, obtaining the energy storage capacity to be stored in the energy consumption time period; step S122, dividing the target area of the energy consumption time period according to the energy consumption characteristics, obtaining the energy consumption time period energy consumption type distribution, energy consumption characteristics include single household energy consumption, single commercial energy consumption, single processing and manufacturing 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, according to the energy storage status of the molten salt energy storage system in the energy consumption period, the energy storage capacity to be stored in the energy consumption period, the energy storage status 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 of the energy consumption period, re-determine the energy storage operation strategy of the molten salt energy storage system in the energy consumption period; according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods, determine the energy storage operation strategy of the molten salt energy storage system in the target area during the preset time period.

[0066] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0067] First, the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0068] The present invention provides Figure 1 The working method of the molten salt energy storage system based on energy consumption end power prediction shown includes steps S11-S13:

[0069] Step S11, obtain the energy consumption data of the target area in the historical time period, and divide the historical time period into time periods according to the meteorological data in the historical time period to obtain several energy consumption time periods, wherein the duration n of each energy consumption time period is greater than 1 hour, and n is an integer, and the meteorological data includes rainfall data, light data and wind data.

[0070] The target area may refer to an area with relatively complete supporting facilities and a large scale. In the target area, energy can be obtained through a variety of power generation units, such as thermal power units, hydropower units, wind power units, and solar power units.

[0071] In the target area, there can be several types of energy storage facilities used to regulate 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).

[0072] The molten salt energy storage system is an energy storage technology that uses molten salt as a thermal energy storage medium. The molten salt energy storage system converts solar radiation into thermal energy and uses molten salt to store this energy so that it can be converted into electrical energy when needed.

[0073] The historical time period is divided into several energy consumption periods based on the meteorological data within the historical time period, including:

[0074] Data preprocessing is performed on meteorological data within a historical period, wherein data preprocessing includes missing value processing, outlier processing and data standardization processing.

[0075] Historical meteorological data can be meteorological data for the target area over the past few years. This data may 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.

[0076] Several typical meteorological characteristics are constructed, including at least: rainy day characteristics, sunny day characteristics, cloudy day characteristics and snowy day characteristics.

[0077] Typical meteorological characteristics are those that represent different weather conditions.

[0078] For example:

[0079] Rainy day characteristics: average rainfall over a period of time or a day.

[0080] Sunny day characteristics: the cumulative number of hours with sufficient sunshine in a day or a period of time.

[0081] Snowy characteristics: Average snowfall over a day or period of time.

[0082] Cloudy characteristics: cloud cover over a day or period of time.

[0083] In addition, combined features can be constructed, such as the clearness index (combining sunlight and rainfall) and the extreme weather index (heavy rainfall and strong wind, etc.) to better capture complex weather patterns.

[0084] It is understandable that the purpose of constructing several typical meteorological characteristics is not only to distinguish the weather in each period, but also to combine each type of unit with the weather characteristics. (Each type of unit has different output efficiency in different weather conditions)

[0085] Determine the preset number of clusters; under the K-means algorithm, divide the historical time period into several energy consumption periods based on several typical meteorological characteristics and the preset number of clusters.

[0086] K-means is a commonly used clustering algorithm. K-means distributes data points into k clusters through iterative optimization to minimize the square error within the cluster.

[0087] The clustering effect under different k values can be evaluated by the Elbow Method or Silhouette Score, so as to select the optimal preset number of clusters.

[0088] Randomly select k data points as the initial centroids, calculate the distance of each data point to all centroids, and assign it to the nearest cluster; then recalculate the position of the centroid based on all points in the cluster. Repeat this process until the centroid no longer changes significantly.

[0089] After clustering is completed, the characteristics of each cluster can be analyzed (that is, the typical meteorological characteristics represented by each cluster), and based on this, the typical meteorological characteristics of each hour in the historical period can be defined, and consecutive hours with the same typical meteorological characteristics can be divided into an energy consumption period.

[0090] The present invention divides time periods according to different weather characteristics, can identify energy consumption patterns under different weather modes, and combine the energy consumption patterns under different weather modes to more accurately formulate charging and discharging strategies for the molten salt energy storage system.

[0091] Step S12: Execute steps S121-S123 for each energy consumption period.

[0092] Step S121 : Obtain the energy storage capacity for 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.

[0093] Specifically include:

[0094]

[0095] in, For the The energy storage capacity for each energy consumption period, For the The sum of energy consumption during each energy consumption period, For the The unit output during each energy consumption period.

[0096] The sum of energy consumption during the energy consumption period refers to all energy consumption in the target area during the energy consumption period, and the unit output during the energy consumption period refers to the unit output of the generator set in the target area during the energy consumption period.

[0097] when When >0, it means that the energy storage system needs to be used for energy storage; when When <0, it means that the energy storage system needs to be used to deliver energy. When it is equal to 0, no storage and no delivery.

[0098] Step S122: Divide the target area of the energy usage period by energy usage characteristics to obtain the energy usage type distribution of the energy usage period. Energy usage characteristics include single residential energy use, single commercial energy use, single manufacturing energy use, residential and commercial energy use, residential and manufacturing energy use, commercial and manufacturing energy use, and mixed commercial, residential, and manufacturing energy use. (Municipal energy use can be recorded as residential energy use.)

[0099] Energy usage characteristics refer to the type of energy usage in a particular area. For example, consider a building A in a particular area. If all floors of A are residential, the area occupied by A is characterized by residential energy use alone. If the first floor of A is commercial, and floors 2 and above are residential, the area occupied by A is characterized by residential and commercial energy use. If the first floor of A is commercial, floors 2-5 are production lines, and floors 6 and above are residential, the area occupied by A is characterized by a mixed use of commercial, residential, and manufacturing energy.

[0100] The target area of the energy consumption period is divided according to the energy consumption characteristics to obtain the energy consumption type distribution of the energy consumption period, including:

[0101] The target area of the energy usage period is divided according to the energy usage characteristics to obtain sub-areas corresponding to each energy usage characteristic.

[0102] According to the above example, the target area is divided in sequence to obtain several sub-areas.

[0103] The energy consumption of the sub-area under each energy consumption characteristic in the energy consumption period is determined 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-area under each energy consumption characteristic in the energy consumption period.

[0104] According to the energy consumption of the sub-areas under several energy consumption characteristics in the energy consumption period, the energy consumption proportions of several energy consumption characteristics in the energy consumption period are determined, and the energy consumption proportions of the energy consumption period are used as the energy consumption type distribution of the energy consumption period.

[0105] Compare the energy consumption of each energy consumption characteristic in a certain energy consumption period to obtain the energy consumption proportion.

[0106] For example, in a certain energy consumption period, the energy consumption proportions of single living energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, living and commercial energy consumption, living and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, living and processing and manufacturing energy consumption are = 0.15:0.08:0.21:0.34:0.03:0.17:0.02.

[0107] Step S123, re-determine the energy storage operation strategy of the molten salt energy storage system in the energy usage period based on the energy storage status 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 status of the remaining energy storage systems in the energy usage period, the energy consumption type distribution in the energy usage period, and the meteorological characteristics of the energy usage period.

[0108] It should be noted that the target area is a relatively mature city, which means that the energy storage facilities are relatively complete. As 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 than that of battery energy storage systems. Therefore, in the present invention, it is believed that the proportion of other energy storage systems is much larger than that of the molten salt energy storage system, and in >0, > .

[0109] Specifically include:

[0110] when >0 and hour:

[0111]

[0112] Among them, if If it is less than 0, no energy storage is performed. If it is greater than 0, energy storage is performed;

[0113] when >0 and hour:

[0114]

[0115] in, For the The charging and discharging strategy of the molten salt energy storage system in each energy consumption period, For the The energy storage capacity for each energy consumption period, For the The energy storage status of the molten salt energy storage system in each energy consumption period, For the The energy storage status of the remaining energy storage systems in each energy consumption period, is a preset constant. When the weather characteristic of the energy consumption period is sunny, it is recorded as 0.9, and other weather characteristics are recorded as 0.5; A constant is preset for the energy consumption type distribution. When the energy consumption proportion of single processing and manufacturing energy, life and processing and manufacturing energy, commercial and processing and manufacturing energy, and commercial, life and processing and manufacturing mixed energy in the energy consumption type distribution in the energy consumption period is greater than the preset threshold, It is recorded as 0.3, otherwise it is recorded as 0.8; the preset threshold is 0.5, etc., which can be determined according to actual conditions.

[0116] when 0:00

[0117]

[0118] in, is the maximum capacity of the molten salt energy storage system.

[0119] The energy storage status 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 status of the remaining energy storage systems refers to the remaining energy storage capacity of the remaining energy storage systems.

[0120] It should be noted that The charging and discharging strategy of the molten salt energy storage system in each energy consumption period does not refer to the The charging and discharging strategy of the molten salt energy storage system in the energy consumption period is to re- The charging and discharging strategies of the molten salt energy storage system in each energy consumption period are determined.

[0121] The present invention redefines The charging and discharging strategy of the molten salt energy storage system in each energy consumption period can provide more accurate guidance for the prediction of the charging and discharging strategy of the molten salt energy storage system.

[0122] By integrating energy storage status, storage capacity and multi-system collaborative data, the present invention can optimize the storage / release rhythm, fully utilize the large heat capacity of the molten salt system, optimize the heat storage efficiency during the grid off-peak period, and accurately match high-grade energy demand such as industrial heating during peak periods.

[0123] The present invention combines regional energy consumption types and time period characteristics to achieve multi-energy coupling supply. By real-time monitoring of the status of other energy storage systems, a complementary mechanism is established to give full play to the peak-shaving capability of molten salt energy storage when the short-term energy storage system is overloaded.

[0124] The dynamic optimization process provided by this invention effectively addresses the problems of wasted heat storage or insufficient energy supply caused by traditional fixed strategies. Due to the slow thermal response of molten salt systems, this invention, based on multi-source data modeling and prediction, can mitigate the delays caused by thermal inertia. Furthermore, through multi-system coordinated scheduling, it significantly improves renewable energy absorption capacity and overall economic efficiency.

[0125] Step S13: determining 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 strategy of the molten salt energy storage system in several energy usage time periods.

[0126]

Method 1

[0127] The energy storage operation strategy of the molten salt energy storage system during the energy consumption period, the meteorological characteristics of the energy consumption period, and the energy consumption type distribution during the energy consumption period are bound into a data group, thereby obtaining a total of several data groups;

[0128] Inputting several data sets into a neural network to be trained to train the neural network, saving the latest neural network parameters when preset training requirements are met, and obtaining a target neural network, wherein the neural network to be trained is used to predict the energy storage operation strategy of the molten salt energy storage system;

[0129] The predicted meteorological characteristics and predicted energy consumption type distribution for the preset time period are obtained and input into the target neural network to obtain the energy storage operation strategy of the molten salt energy storage system for the preset time period.

[0130] Preset training requirements can include a maximum number of training sessions and a threshold for achieving a target rate, which are not limited here. The preset time period can be a future time period that is not far from the current time to facilitate prediction of meteorological characteristics and energy consumption type distribution.

[0131]

Method 2

[0132] The preset time period is divided into m preset time sub-segments, and the length of the preset time sub-segment is 1 hour; the predicted meteorological data of each preset time sub-segment is matched with the meteorological characteristics of several energy consumption periods in turn for meteorological similarity, and several target energy consumption periods are screened out, where one preset time sub-segment can correspond to several target energy consumption periods.

[0133] When the similarity between a certain predicted meteorological data and the meteorological characteristics of the energy consumption period is greater than the weather similarity threshold, the two can be matched.

[0134] The predicted energy consumption type distribution corresponding to the preset time sub-segment is matched with the energy consumption type distribution of several energy consumption time periods to obtain the target energy consumption type distribution of the preset time sub-segment.

[0135] Based on the above similarity processing method, when the predicted energy consumption type distribution corresponding to a 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 the preset time sub-segment can include multiple.

[0136] Based on the target energy consumption period corresponding to each preset time sub-segment and the corresponding target energy consumption type distribution, the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time sub-segment is determined; specifically, the target energy consumption type distribution belonging to the preset time sub-segment is screened out from several target energy consumption periods, and the highest value of the product of the similarities between the two is used as the energy storage operation strategy corresponding to the final energy consumption period.

[0137] For example, there are 150 target energy consumption time periods, among which there are 50 energy consumption types belonging to this energy consumption time period. That is to say, among the 50, the highest value is determined by the similarity of meteorological characteristics × 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 is, that is, the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time sub-segment.

[0138] According to the energy storage operation strategies of the molten salt energy storage system corresponding to the plurality of preset time subsegments, the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period is determined.

[0139] By splitting the time periods and performing similarity calculations based on meteorological conditions and characteristics, the present invention can provide an accurate basis for predicting the energy storage operation strategy of future molten salt energy storage systems.

[0140] 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 comprising: obtaining energy consumption data of a target area in a historical time period, and dividing the historical time period into time periods according to meteorological data in the historical time period, to obtain a number of energy consumption time periods, wherein 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, light data, and wind data; for each energy consumption time period, executing steps S121-S123, comprising: step S121, obtaining the energy storage capacity to be stored in the energy consumption time period according to the sum of the 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 the energy consumption time period according to the energy consumption characteristics, to obtain The energy consumption type distribution in the energy consumption period includes single living energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, living and processing and manufacturing energy consumption, commercial and processing and manufacturing energy consumption, and mixed commercial, living and processing and manufacturing energy consumption; step S123, according to the energy storage status of the molten salt energy storage system in the energy consumption period, the energy storage capacity to be stored in the energy consumption period, the energy storage status 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 of the energy consumption period, re-determine the energy storage operation strategy of the molten salt energy storage system in the energy consumption period; according to the energy storage operation strategies of the molten salt energy storage system in several energy consumption periods, determine the energy storage operation strategy of the molten salt energy storage system in the target area during the preset time period.

[0141] The present invention divides time periods according to different weather characteristics, can identify energy consumption patterns under different weather modes, and combine the energy consumption patterns under different weather modes to more accurately formulate charging and discharging strategies for the molten salt energy storage system.

[0142] The present invention redefines The charging and discharging strategy of the molten salt energy storage system in each energy consumption period can provide more accurate guidance for the prediction of the charging and discharging strategy of the molten salt energy storage system.

[0143] By integrating energy storage status, storage capacity and multi-system collaborative data, the present invention can optimize the storage / release rhythm, fully utilize the large heat capacity of the molten salt system, optimize the heat storage efficiency during the grid off-peak period, and accurately match high-grade energy demand such as industrial heating during peak periods.

[0144] The present invention combines regional energy consumption types and time period characteristics to achieve multi-energy coupling supply. By real-time monitoring of the status of other energy storage systems, a complementary mechanism is established to give full play to the peak-shaving capability of molten salt energy storage when the short-term energy storage system is overloaded.

[0145] The dynamic optimization process provided by this invention effectively addresses the problems of wasted heat storage or insufficient energy supply caused by traditional fixed strategies. Due to the slow thermal response of molten salt systems, this invention, based on multi-source data modeling and prediction, can mitigate the delays caused by thermal inertia. Furthermore, through multi-system coordinated scheduling, it significantly improves renewable energy absorption capacity and overall economic efficiency.

[0146] By splitting the time periods and performing similarity calculations based on meteorological conditions and characteristics, the present invention can provide an accurate basis for predicting the energy storage operation strategy of future molten salt energy storage systems.

[0147] Based on the same inventive concept, the present invention provides Figure 2 The molten salt energy storage system working device shown in the figure, based on the power prediction of the energy consumption end, includes:

[0148] The data acquisition module 21 is used to obtain energy consumption data of the target area within a historical time period, and divide the historical time period into several energy consumption periods according to the meteorological data within 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, sunlight data, and wind data;

[0149] The first strategy module 22 is used to execute steps S121-S123 for each energy consumption period, including: step S121, 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, obtain the energy storage capacity to be stored in the energy consumption period; step S122, divide the target area of the energy consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution of the energy consumption period, the energy consumption characteristics including single living energy consumption, single commercial energy consumption, single processing and manufacturing energy consumption, living and commercial 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; step S123, according to the energy storage status of the molten salt energy storage system in the energy consumption period, the energy storage capacity to be stored in the energy consumption period, the energy storage status 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 of the energy consumption period, re-determine the energy storage operation strategy of the molten salt energy storage system in the energy consumption period;

[0150] The second strategy module 23 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 based on the energy storage operation strategy of the molten salt energy storage system in several energy usage time periods.

[0151] Based on the same inventive concept, the present application also provides an electronic device as shown, including:

[0152] processor;

[0153] a memory for storing processor-executable instructions;

[0154] Among them, the processor is configured to execute to implement the molten salt energy storage system working method based on energy-consuming end power prediction as provided above.

[0155] Based on the same inventive concept, the present application also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device is enabled to execute the molten salt energy storage system operating method based on energy consumption end power prediction as provided above.

[0156] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiment of the present invention, based on the information processing method described in the embodiment of the present invention, those skilled in the art will be able to understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention will not be described in detail here. As long as the electronic device used by those skilled in the art to implement the information processing method in the embodiment of the present invention falls within the scope of protection of the present invention.

[0157] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0161] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0162] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A molten salt energy storage system operating method based on energy consumption end power prediction is characterized in that: The method comprises: Obtain energy consumption data for the target area during a historical time period, and divide the historical time period into several energy consumption periods based on the meteorological data during the historical time period. The duration n of each energy consumption period is greater than 1 hour, and n is an integer. The meteorological data includes rainfall data, sunlight 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: Divide the target area of the energy usage period by energy usage characteristics to obtain the energy usage type distribution of the energy usage period, wherein the energy usage characteristics include single residential energy usage, single commercial energy usage, single manufacturing energy usage, residential and commercial energy usage, residential and manufacturing energy usage, manufacturing and manufacturing energy usage, and mixed manufacturing, residential and manufacturing energy usage. Step S123, re-determining the energy storage operation strategy of the molten salt energy storage system for the energy usage period based on the energy storage status of the molten salt energy storage system for the energy usage period, the energy storage capacity to be stored for the energy usage period, the energy storage status of the remaining energy storage systems for the energy usage period, the energy consumption type distribution for the energy usage period, and the meteorological characteristics for the energy usage period; According to the energy storage operation strategy of the molten salt energy storage system in several energy consumption time periods, the energy storage operation strategy of the molten salt energy storage system in the target area in the preset time period is determined.

2. The method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 1, characterized in that: The target area of the energy usage period is divided according to the energy usage characteristics to obtain the energy usage type distribution of the energy usage period, including: Dividing the target area of the energy usage period by energy usage characteristics to obtain sub-areas corresponding to each energy usage characteristic; Determine the energy consumption of the sub-area under each energy consumption characteristic during the energy consumption period; According to the energy consumption of the sub-areas under several energy consumption characteristics in the energy consumption period, the energy consumption proportions of several energy consumption characteristics in the energy consumption period are determined, and the energy consumption proportions of the energy consumption period are used as the energy consumption type distribution of the energy consumption period.

3. The method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 2, characterized in that: Based on the energy storage status of the molten salt energy storage system during the energy usage period, the energy storage capacity to be stored during the energy usage period, the energy storage status of the remaining energy storage systems during the energy usage period, the energy consumption type distribution during the energy usage period, and the meteorological characteristics during the energy usage period, the energy storage operation strategy of the molten salt energy storage system during the energy usage period is re-determined, including: when >0 and hour: Among them, if If it is less than 0, no energy storage is performed. If it is greater than 0, energy storage is performed; when >0 and hour: in, For the The charging and discharging strategy of the molten salt energy storage system in each energy consumption period, For the The energy storage capacity for each energy consumption period, For the The energy storage status of the molten salt energy storage system in each energy consumption period, For the The energy storage status of the remaining energy storage systems in each energy consumption period, is a preset constant. When the weather characteristic of the energy consumption period is sunny, it is recorded as 0.9, and other weather characteristics are recorded as 0.5; A constant is preset for the energy consumption type distribution. When the energy consumption proportion of single processing and manufacturing energy, life and processing and manufacturing energy, commercial and processing and manufacturing energy, and commercial, life and processing and manufacturing mixed energy in the energy consumption type distribution in the energy consumption period is greater than the preset threshold, Recorded as 0.3, otherwise recorded as 0.8; when 0:00 in, is the maximum capacity of the molten salt energy storage system.

4. The method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 1, wherein: The historical time period is divided into several energy consumption periods based on the meteorological data within the historical time period, including: Perform data preprocessing on meteorological data within the historical time period, where data preprocessing includes missing value processing, outlier processing and data standardization; Constructing several typical meteorological characteristics, including at least: rainy day characteristics, sunny day characteristics, cloudy day characteristics, and snowy day characteristics; 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 characteristics and the preset number of clusters.

5. The method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 1, characterized in that: Determining the energy storage operation strategy of the molten salt energy storage system in the target area during a preset time period based on the energy storage operation strategy of the molten salt energy storage system during a plurality of energy usage time periods includes: The energy storage operation strategy of the molten salt energy storage system during the energy consumption period, the meteorological characteristics of the energy consumption period, and the energy consumption type distribution during the energy consumption period are bound into a data group, thereby obtaining a total of several data groups; Inputting a plurality of data groups into a neural network to be trained to train the neural network, saving the latest neural network parameters when preset training requirements are met, and obtaining a target neural network, wherein the neural network to be trained is used to predict the energy storage operation strategy of the molten salt energy storage system; The predicted meteorological characteristics and predicted energy consumption type distribution for a preset time period are obtained and input 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 method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 1, characterized in that: Determining the energy storage operation strategy of the molten salt energy storage system in the target area during a preset time period based on the energy storage operation strategy of the molten salt energy storage system during the plurality of energy usage time periods further includes: Divide the preset time period into m preset time sub-segments, and the length of the preset time sub-segments is 1 hour; The predicted meteorological data of each preset time sub-segment is sequentially matched with the meteorological characteristics of several energy consumption periods for meteorological similarity, and several target energy consumption periods are selected, wherein one preset time sub-segment corresponds to several target energy consumption periods; Performing energy distribution similarity matching based on the predicted energy consumption type distribution corresponding to the preset time sub-segment and the energy consumption type distribution of several energy consumption periods to obtain the target energy consumption type distribution for the preset time sub-segment; Determine the energy storage operation strategy of the molten salt energy storage system corresponding to each preset time subsegment based on the target energy consumption period corresponding to each preset time subsegment and the corresponding target energy consumption type distribution; According to the energy storage operation strategies of the molten salt energy storage system corresponding to the plurality of preset time subsegments, the energy storage operation strategy of the molten salt energy storage system corresponding to the preset time period is determined.

7. The method for operating a molten salt energy storage system based on energy consumption end power prediction according to claim 1, characterized in that: 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, the energy storage capacity to be stored in the energy consumption period is obtained, including: in, For the The energy storage capacity for each energy consumption period, For the The sum of energy consumption during each energy consumption period, For the The unit output during each energy consumption period.

8. A molten salt energy storage system operating device based on energy consumption end power prediction is characterized in that: The device comprises: A data acquisition module is used to obtain energy consumption data of a target area within a historical time period, and divide the historical time period into several energy consumption periods according to the meteorological data within 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, sunlight 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 the energy consumption period according to the sum of the 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 consumption period according to the energy consumption characteristics to obtain the energy consumption type distribution of the energy consumption period, wherein the energy consumption characteristics include single living energy, single commercial energy, single processing and manufacturing energy, living and commercial energy, living and processing and manufacturing energy, commercial and processing and manufacturing energy, and commercial, living and processing and manufacturing mixed energy; 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 status of the molten salt energy storage system in the energy consumption period, the energy storage capacity to be stored in the energy consumption period, the energy storage status 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 of the 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 based on the energy storage operation strategy of the molten salt energy storage system in several energy consumption time periods.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute to implement the molten salt energy storage system operating method based on energy consumption end power prediction 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 is enabled to implement the molten salt energy storage system operating method based on energy-consuming end power prediction as described in any one of claims 1 to 7.

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