Energy management and control platform system and control method thereof

Abstract

The present application provides an energy management and control platform system and a control method thereof, which determines the power fluctuation entropy of the allocation unit when the energy is switched by extracting the transient power fluctuation amount and the power distribution information under the stable power distribution state from the historical energy allocation data; determines the power imbalance when the load changes in the corresponding time period according to the configuration information of the photoelectric storage capacity in different time periods, and determines the capacity compensation cost of the configuration unit when configuring the photoelectric storage capacity in different time periods through all the power imbalances and the change characteristics of the photoelectric storage capacity configuration; determines the energy allocation prediction amount, and then adjusts the energy allocation prediction amount through the power fluctuation entropy and all the capacity compensation costs to obtain the energy prediction adjustment value; and adaptively controls the photoelectric storage capacity configuration of the configuration unit based on the energy prediction adjustment value. The scheme of the present application can realize the capacity adaptive configuration when the photoelectric storage energy fluctuates transiently, thereby improving the stability of energy allocation.

Description

Technical Field

[0001] The present application relates to the field of energy management and control technology, and more specifically, to an energy management and control platform system and a control method thereof. Background Art

[0002] Energy management and control refers to the monitoring and optimization of energy production, distribution and use through a series of technical means and management strategies to improve energy efficiency, reduce energy consumption and lower energy costs, while reducing the impact on the environment. Energy management and control technology is not only applicable to industrial enterprises, but also widely used in commercial buildings, public facilities and household energy management. Through the comprehensive application of these technologies, energy conservation and emission reduction, cost reduction and improved energy utilization efficiency can be effectively achieved.

[0003] In the photovoltaic-energy storage power supply system, due to the intermittent nature of photovoltaic power generation and the volatility of the load, it is difficult to achieve a dynamic balance between supply and demand between power supply and load, which greatly threatens the stable operation of the power supply system. The photovoltaic-energy storage capacity configuration can smooth out this supply and demand difference, thereby improving energy utilization. However, during the energy switching process, the load change causes transient fluctuations in electricity, which makes the distribution system extremely unstable, resulting in energy waste and even power outages. By pre-compensating the capacity configuration cost during the energy switching process, the impact of power fluctuations on the stability of the distribution system during the photovoltaic energy storage switching process can be reduced. Therefore, how to achieve adaptive capacity configuration during transient fluctuations in photovoltaic energy storage, thereby improving the stability of energy allocation, has become a major problem facing the industry. Summary of the invention

[0004] The present application provides an energy management and control platform system and a control method thereof, which can realize capacity adaptive configuration when photovoltaic energy storage fluctuates transiently, thereby improving the stability of energy allocation.

[0005] In a first aspect, the present application provides a control method for an energy management and control platform, comprising the following steps:

[0006] Collect energy allocation information from the target energy management and control platform, and then obtain historical energy allocation data;

[0007] Extracting the transient fluctuation amount of power of the dispatching unit in the target energy management and control platform at each energy switch from the historical energy dispatching data, and then determining the power fluctuation entropy of the dispatching unit when performing energy switching from the transient fluctuation amount of all power and the power distribution information under the stable power distribution state;

[0008] Obtain the configuration information of the photovoltaic storage capacity in different time periods, determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the photovoltaic storage capacity in each time period, and determine the capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods through all the power imbalances and the change characteristics of the photovoltaic storage capacity configuration;

[0009] Predicting the energy allocation amount of the target energy management and control platform according to the historical energy allocation data to obtain the energy allocation prediction amount, and then adjusting the energy allocation prediction amount according to the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value;

[0010] The photoelectric storage capacity configuration of the configuration unit is adaptively regulated based on the energy prediction adjustment value.

[0011] In some embodiments, extracting the transient fluctuation amount of power of the allocation unit in the target energy management and control platform at each energy switch from the historical energy allocation data specifically includes:

[0012] Determine a switching node for each energy switch according to the historical energy allocation data;

[0013] Extracting energy allocation information before and after each switching node is switched from the historical energy allocation data;

[0014] The transient fluctuation of power when the energy of the corresponding switching node is switched is determined through each energy allocation information.

[0015] In some embodiments, determining the power fluctuation entropy of the dispatching unit when performing energy switching based on the transient fluctuation amount of all power and the power distribution information under the stable power distribution state specifically includes:

[0016] Determine the baseline fluctuation of energy in the energy allocation process through the power distribution information under the stable power distribution state;

[0017] Determine the transient fluctuation variance according to the transient fluctuation amount of all electric power;

[0018] The power fluctuation entropy of the dispatching unit when performing energy switching is determined according to the transient fluctuation amount of all power, the reference fluctuation amount and the transient fluctuation variance.

[0019] In some embodiments, determining the power imbalance degree when the load changes in the corresponding time period according to the configuration information of the solar energy storage capacity in each time period specifically includes:

[0020] Select a time period from each time period as a designated time period, and then obtain configuration information of the optical storage capacity of the designated time period;

[0021] Obtaining the load demand power when the load changes during the specified time period;

[0022] Extracting the load power when the load changes from the configuration information of the photovoltaic storage capacity during the specified time period;

[0023] Determine the power imbalance degree when the load changes during the specified time period by using the load demand power and the load power;

[0024] Repeat the above steps to obtain the power imbalance degree when the load changes in the remaining time period.

[0025] In some embodiments, the capacity compensation cost of configuring the photovoltaic storage capacity in different time periods by the configuration unit in the target energy management and control platform is determined by all the power imbalances and the change characteristics of the photovoltaic storage capacity configuration, specifically including:

[0026] Obtain configuration information of optical storage capacity at different time periods;

[0027] Determine the standard deviation of the configured power and the variability of the configured power when configuring the optical storage capacity through the configuration information of the optical storage capacity in different time periods;

[0028] Determine a trend factor of configuration power change when configuring the optical storage capacity, and use the trend factor as a change feature of the optical storage capacity configuration;

[0029] The capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods is determined according to the standard deviation of the configured power, the variability of the configured power, the trend factor and all power imbalances.

[0030] In some embodiments, predicting the energy allocation amount of the target energy management and control platform based on the historical energy allocation data to obtain the energy allocation prediction amount specifically includes:

[0031] Train the energy allocation prediction model by using a specified amount of historical energy allocation data;

[0032] Using current energy allocation data as input parameters of the energy allocation prediction model;

[0033] Then, the energy allocation prediction model is used to output the energy allocation prediction amount.

[0034] In some embodiments, adaptively regulating the light storage capacity configuration of the configuration unit based on the energy prediction adjustment value specifically includes:

[0035] Converting the energy forecast adjustment value into a configuration instruction of the light storage capacity;

[0036] The optical storage capacity configuration of the configuration unit is adjusted through the configuration instruction.

[0037] In a second aspect, the present application provides an energy management and control platform system, including:

[0038] The acquisition module is used to collect energy allocation information of the target energy management and control platform, and then obtain historical energy allocation data;

[0039] A processing module, used to extract the transient fluctuation amount of the power of the allocation unit in the target energy management and control platform at each energy switch from the historical energy allocation data, and then determine the power fluctuation entropy of the allocation unit when performing energy switching based on the transient fluctuation amount of all power and the power distribution information under the stable power distribution state;

[0040] The processing module is further used to obtain configuration information of the optical storage capacity in different time periods, determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the optical storage capacity in each time period, and determine the capacity compensation cost when the configuration unit in the target energy management and control platform configures the optical storage capacity in different time periods through all the power imbalances and the change characteristics of the optical storage capacity configuration;

[0041] The processing module is further used to predict the energy allocation amount of the target energy management and control platform according to the historical energy allocation data to obtain the energy allocation prediction amount, and then adjust the energy allocation prediction amount through the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value;

[0042] An execution module is used to adaptively control the light storage capacity configuration of the configuration unit based on the energy prediction adjustment value.

[0043] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the control method of the above-mentioned energy management and control platform.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the control method of the above-mentioned energy management and control platform is implemented.

[0045] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0046] In an embodiment of the present application, energy allocation information of a target energy management and control platform is collected to obtain historical energy allocation data; the transient fluctuation amount of electric power of an allocation unit in the target energy management and control platform at each energy switch is extracted from the historical energy allocation data, and then the power fluctuation entropy of the allocation unit when performing energy switching is determined by the transient fluctuation amount of all electric power and the distribution information under a stable distribution state; the configuration information of the optical storage capacity in different time periods is obtained, and the power imbalance when the load changes in the corresponding time period is determined according to the configuration information of the optical storage capacity in each time period, and the capacity compensation cost when the configuration unit in the target energy management and control platform configures the optical storage capacity in different time periods is determined according to the change characteristics of all power imbalances and optical storage capacity configuration; the energy allocation amount of the target energy management and control platform is predicted according to the historical energy allocation data to obtain an energy allocation prediction amount, and then the energy allocation prediction amount is adjusted according to the power fluctuation entropy and all capacity compensation costs to obtain an energy prediction adjustment value; the configuration of the optical storage capacity of the configuration unit is adaptively controlled based on the energy prediction adjustment value.

[0047] It can be seen that the present application adjusts the energy allocation prediction amount through the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value, and then adaptively controls the photovoltaic storage capacity configuration of the configuration unit based on the energy prediction adjustment value; first, the transient fluctuation amount of power at each switching node when the energy is switched is determined through the historical energy allocation data, and the power fluctuation entropy is used to measure the uncertainty of power fluctuation during energy switching in the power system, and then the uncertainty of power fluctuation can better understand the power fluctuation during energy switching, which is conducive to determining the optimal photovoltaic storage capacity configuration strategy; secondly, by quantifying the power imbalance caused by changes in load demand and fluctuations in renewable energy output, the energy management and control system can be controlled according to the quantified power imbalance. By compensating for the cost of configuring the photovoltaic storage capacity, it is possible to dynamically respond to the impact of changes in load demand and fluctuations in renewable energy on the stability of the power system, and further improve the stability and reliability of the power system; finally, the energy allocation forecast is adjusted according to the fluctuation of electricity and the compensation cost when configuring the photovoltaic storage capacity, and an energy forecast adjustment value is obtained, and then the photovoltaic storage capacity configuration of the configuration unit is pre-adaptively adjusted by the energy forecast adjustment value, which is beneficial to reduce the impact of instability in the power system caused by transient fluctuations in electricity during energy switching, and further reduce the impact of additional power consumed during photovoltaic storage capacity configuration on the stability of the power system; in summary, the embodiments of the present application can realize capacity adaptive configuration when photovoltaic storage energy fluctuates transiently, thereby improving the stability of energy allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an exemplary flow chart of a control method of an energy management and control platform according to some embodiments of the present application;

[0049] Figure 2 is a schematic diagram of a process for determining a transient fluctuation amount according to some embodiments of the present application;

[0050] Figure 3 is a schematic diagram of a process for determining energy prediction adjustment values ​​according to some embodiments of the present application;

[0051] Figure 4 is a schematic diagram of exemplary hardware and / or software of an energy management and control platform system according to some embodiments of the present application;

[0052] Figure 5 It is a structural schematic diagram of a computer device for implementing a control method for an energy management and control platform according to some embodiments of the present application. DETAILED DESCRIPTION

[0053] The core of this application is to determine the power fluctuation entropy of the allocation unit when the energy is switched by extracting the transient power fluctuation amount of the allocation unit each time the energy is switched and the distribution information under the stable distribution state from the historical energy allocation data; determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the photovoltaic storage capacity in different time periods, and determine the capacity compensation cost of the configuration unit when configuring the photovoltaic storage capacity in different time periods through all the power imbalances and the change characteristics of the photovoltaic storage capacity configuration; determine the energy allocation prediction amount, and then adjust the energy allocation prediction amount through the power fluctuation entropy and all the capacity compensation costs to obtain the energy prediction adjustment value; based on the energy prediction adjustment value, the photovoltaic storage capacity configuration of the configuration unit is adaptively controlled; the adoption of this scheme can realize the capacity adaptive configuration when the photovoltaic storage energy fluctuates transiently, thereby improving the stability of energy allocation.

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a control method of an energy management and control platform according to some embodiments of the present application. The control method 100 of the energy management and control platform mainly includes the following steps:

[0055] In step 101, energy allocation information of a target energy management and control platform is collected to obtain historical energy allocation data.

[0056] In specific implementation, the energy allocation information of the target energy management and control platform is collected, and then the historical energy allocation data is obtained. This can be achieved in the following way, namely: the allocation information in the energy allocation process is monitored by the monitoring unit in the target energy management and control platform according to a preset sampling frequency, so as to obtain the energy allocation information of each monitoring, wherein the energy allocation information includes: the distribution power of the photovoltaic end, the discharge power of the chemical energy storage end, the charging power of the chemical energy storage end and the compensation power of the supercapacitor, and all the energy allocation information is stored in the storage unit in the target energy management and control platform, and then the historical energy allocation data can be obtained from the storage unit, wherein the historical energy allocation data refers to a set of energy allocation information collected in the past six months, and in other embodiments, the historical energy allocation data may also refer to a set of energy allocation information collected in other time periods, which is not limited here.

[0057] It should be noted that the value range of the preset sampling frequency described in the present application is between 10 Hz and 50 Hz. In other embodiments, the value range of the preset sampling frequency can also be set to other ranges, which is not limited here.

[0058] In step 102, the transient fluctuation amount of electric power of the allocation unit in the target energy management and control platform at each energy switch is extracted from the historical energy allocation data, and then the electric power fluctuation entropy of the allocation unit when performing energy switching is determined based on the transient fluctuation amount of all electric power and the distribution information under the stable distribution state.

[0059] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the transient fluctuation amount in some embodiments of the present application. In this embodiment, the transient fluctuation amount of the power of the allocation unit in the target energy management and control platform at each energy switch is extracted from the historical energy allocation data, which can be achieved by the following steps:

[0060] First, in step 1021, a switching node for each energy switching is determined according to the historical energy allocation data;

[0061] Then, in step 1022, the energy allocation information before and after the switching of each switching node is intercepted from the historical energy allocation data;

[0062] Finally, in step 1023, the transient fluctuation amount of the power when the energy of the corresponding switching node is switched is determined through each energy allocation information.

[0063] In specific implementation, determining the switching node at each energy switch based on the historical energy allocation data can be implemented in the following manner, namely: initialize a switching node detection model, use the historical energy allocation data as the initialization parameter of the switching node detection model, and then use the switching node detection model to detect the switching node at each energy switch of the allocation unit in the energy management and control platform. It should be noted that the switching node detection model in the present application is used for the detection of switching nodes. The switching node detection model is a detection model obtained by preprocessing a large amount of historical energy allocation data, feature extraction, model training and verification. The algorithm structure in the model training process can adopt a support vector machine algorithm. In other embodiments, other existing algorithm structures can also be used, which will not be repeated here.

[0064] In addition, in the specific implementation, the energy allocation information before and after the switching of each switching node is intercepted from the historical energy allocation data, which can be implemented in the following manner, namely: the energy allocation information corresponding to the start of the switching to the end of the switching at each switching node is intercepted from the historical energy allocation data; the transient fluctuation amount of power when the energy of the corresponding switching node is switched is determined by each energy allocation information, which can be implemented in the following manner, namely: a switching node is selected, and the allocation power at the start of the switching and the allocation power at the end of the switching are obtained from the energy allocation information corresponding to the switching node, the allocation power = the distribution power of the photovoltaic end + the distribution power of the chemical energy storage energy, the distribution power of the chemical energy storage energy = the discharge power of the chemical energy storage end - the charging power of the chemical energy storage end, the absolute difference between the allocation power at the end of the switching and the allocation power at the start of the switching is used as the transient fluctuation amount of power when the energy of the switching node is switched, and the above steps are repeated to obtain the transient fluctuation amount of power when the energy of the remaining switching nodes is switched.

[0065] It should be noted that the transient fluctuation amount in the present application is a measure of the degree of fluctuation between the energy allocation amount before and after the energy switching of the allocation unit. The larger the transient fluctuation amount, the greater the degree of fluctuation between the energy allocation amount before and after the energy switching of the allocation unit. The smaller the transient fluctuation amount, the smaller the degree of fluctuation between the energy allocation amount before and after the energy switching of the allocation unit. In the energy management and control platform, in order to make the power system as close to stability as possible, it is usually hoped that the transient fluctuation amount during the energy switching process will be as small as possible.

[0066] In some embodiments, determining the power fluctuation entropy of the dispatching unit when performing energy switching based on the transient fluctuation amount of all power and the power distribution information under the stable power distribution state can be implemented by the following steps:

[0067] Determine the baseline fluctuation of energy in the energy allocation process through the power distribution information under the stable power distribution state;

[0068] Determine the transient fluctuation variance according to the transient fluctuation amount of all electric power;

[0069] The power fluctuation entropy of the dispatching unit when performing energy switching is determined according to the transient fluctuation amount of all power, the reference fluctuation amount and the transient fluctuation variance.

[0070] It should be noted that the power distribution information under the stable power distribution state in the present application refers to the energy allocation information of the target energy management and control platform under the stable power distribution state, and the power distribution information under the stable power distribution state includes: the distribution power of the photovoltaic end under the stable power distribution state and the distribution power of the chemical energy storage energy under the stable power distribution state, and the distribution power of the chemical energy storage energy = the discharge power of the chemical energy storage end - the charging power of the chemical energy storage end.

[0071] In specific implementation, the benchmark fluctuation amount in the present application is a measure of the average fluctuation degree of the energy allocation amount under the stable distribution state in the energy allocation process. As a preferred embodiment, the allocation benchmark amount in the energy allocation process can be determined by the distribution information under the stable distribution state. It can be implemented in the following way, namely: the average value of the distribution power in all the distribution information under the stable distribution state can be first determined, and the average value is used as the distribution power mean, and then the average value of the absolute difference between each distribution power in the distribution information and the distribution power mean is used as the allocation benchmark amount in the energy allocation process, and the distribution power refers to the sum of the distribution power of the photovoltaic end and the distribution power of the chemical energy storage energy.

[0072] It should be noted that the transient fluctuation variance in the present application refers to the variance of the transient fluctuation amount. In some embodiments, the determination of the transient fluctuation variance based on the transient fluctuation amount of all electricity can be achieved in the following manner, namely: the variance calculated by the transient fluctuation amount of all electricity can be used as the transient fluctuation variance.

[0073] In a specific implementation, the power fluctuation entropy of the allocation unit when performing energy switching is determined according to the transient fluctuation amount of all electric power, the reference fluctuation amount and the transient fluctuation variance, which can be implemented in the following manner, namely: first, for each transient fluctuation amount of electric power, the difference between the transient fluctuation amount and the reference fluctuation amount is used as the energy switching fluctuation difference in the energy allocation process, and then all the switching fluctuation differences are obtained; secondly, all the switching fluctuation differences are entropy described by the information entropy in the prior art to obtain the switching fluctuation entropy; then, the transient fluctuation variance is used as the adjustment factor of the switching fluctuation entropy, and then the adjusted switching fluctuation entropy is used as the switching fluctuation entropy of the allocation unit when performing energy switching. The power fluctuation entropy when performing energy switching, as a preferred embodiment, takes the transient fluctuation variance as the adjustment factor of the switching fluctuation entropy, and then takes the adjusted switching fluctuation entropy as the power fluctuation entropy of the allocation unit when performing energy switching. It can be achieved in the following way, that is: the natural exponential of the opposite number of the transient fluctuation variance can be used as the adjustment factor, and then the product of the adjustment factor and the switching fluctuation entropy is used as the power fluctuation entropy of the allocation unit when performing energy switching. Adjusting the switching fluctuation entropy by the transient fluctuation variance is beneficial to reducing the influence of the transient fluctuation amplitude of power on the fluctuation uncertainty, thereby improving the accuracy of the power fluctuation entropy when switching energy.

[0074] It should be noted that the power fluctuation entropy in the present application is an indicator to measure the uncertainty of power fluctuation when energy is switched in the power system. The larger the power fluctuation entropy is, the higher the degree of uncertainty in power fluctuation when energy is switched in the power system. The smaller the power fluctuation entropy is, the lower the degree of uncertainty in power fluctuation when energy is switched in the power system.

[0075] In step 103, the configuration information of the photovoltaic storage capacity in different time periods is obtained, and the power imbalance when the load changes in the corresponding time period is determined according to the configuration information of the photovoltaic storage capacity in each time period. The capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods is determined through all the power imbalances and the change characteristics of the photovoltaic storage capacity configuration.

[0076] It should be noted that the energy management and control platform in the present application is mainly used for the regulation of energy between photovoltaic and chemical energy storage. In specific implementation, the configuration information of the light storage capacity in different time periods can be obtained in the following way, namely: a day can be divided into multiple time periods according to the law of photovoltaic power generation, for example, the morning rising period (6 am to 10 am), the noon peak period (10 am to 3 pm), the afternoon decline period (3 pm to 6 pm), and the night silent period (6 pm to 6 am the next day), and then the configuration information of the light storage capacity in different time periods is obtained from the storage unit, and the configuration information includes: the distribution power of the photovoltaic end, the load power of the load, the charging power of the chemical energy storage end, the discharge power of the chemical energy storage end and the compensation power of the supercapacitor. In other embodiments, other configuration information may also be included, which is not limited here.

[0077] In some embodiments, determining the power imbalance degree when the load changes in the corresponding time period according to the configuration information of the photovoltaic storage capacity in each time period can be implemented by the following steps:

[0078] Select a time period from each time period as a designated time period, and then obtain configuration information of the optical storage capacity of the designated time period;

[0079] Obtaining the load demand power when the load changes during the specified time period;

[0080] Extracting the load power when the load changes from the configuration information of the photovoltaic storage capacity during the specified time period;

[0081] Determine the power imbalance degree when the load changes during the specified time period by using the load demand power and the load power;

[0082] Repeat the above steps to obtain the power imbalance degree when the load changes in the remaining time period.

[0083] It should be noted that in this application, the supply and demand will be regarded as a balanced state when the load is stable. When the load changes, the supply and demand state will change, so it is necessary to adjust the supply and demand through the energy management and control platform to achieve a dynamic balance between supply and demand; in addition, the configuration information of the photovoltaic storage capacity corresponding to the specified time period will be used as the configuration information of the photovoltaic storage capacity for the specified time period.

[0084] In specific implementation, obtaining the load demand power when the load changes in the specified time period can be achieved in the following manner, namely: by monitoring the load power in the specified time period at the load end, historical load monitoring data is obtained, and then the load power when the load changes is extracted from the historical load monitoring data, and the extracted load power is used as the load demand power when the load changes in the specified time period. As a preferred embodiment, the load power when the load changes can be extracted from the historical load monitoring data through a load detection model, and the load detection model is a machine learning model for load change detection; extracting the load power when the load changes from the configuration information of the photovoltaic storage capacity in the specified time period can be achieved in the following manner, namely: obtaining the timestamp when the load changes, obtaining the power of the load configured by the timestamp in the configuration information of the photovoltaic storage capacity in the specified time period, and using the power as the load power when the load changes, wherein the load power includes: the distribution power of the photovoltaic end, the charging power of the chemical energy storage end, and the discharge power of the chemical energy storage end.

[0085] In addition, in specific implementation, the power imbalance degree when the load changes in the specified time period can be determined by the load demand power and the load power in the following manner, namely: first, the total output power of the photovoltaic storage can be determined by the load power. As a preferred embodiment, the total output power of the photovoltaic storage can be determined by the load power by the following formula, namely: total output power of the photovoltaic storage = distribution power of the photovoltaic end + discharge power of the chemical energy storage end - charging power of the chemical energy storage end. Secondly, the difference between the load demand power and the total output power of the photovoltaic storage can be used as the power imbalance degree when the load changes in the specified time period, that is, the power imbalance degree = load demand power - total output power of the photovoltaic storage.

[0086] It should be noted that the power imbalance in the present application is an indicator for measuring the balance state of power supply and demand when the load changes. The larger the power imbalance, the more unbalanced the power supply and demand is when the load changes. The smaller the power imbalance, the more balanced the power supply and demand is when the load changes.

[0087] In some embodiments, the capacity compensation cost of configuring the photovoltaic storage capacity in different time periods by the configuration unit in the target energy management and control platform can be determined by using all the power imbalance and the change characteristics of the photovoltaic storage capacity configuration, which can be implemented by the following steps:

[0088] Obtain configuration information of optical storage capacity at different time periods;

[0089] Determine the standard deviation of the configured power and the variability of the configured power when configuring the optical storage capacity through the configuration information of the optical storage capacity in different time periods;

[0090] Determine a trend factor of configuration power change when configuring the optical storage capacity, and use the trend factor as a change feature of the optical storage capacity configuration;

[0091] The capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods is determined according to the standard deviation of the configured power, the variability of the configured power, the trend factor and all power imbalances.

[0092] In specific implementation, the standard deviation of the configured power and the variability of the configured power when the photoelectric storage capacity is configured can be determined by the configuration information of the photoelectric storage capacity in different time periods. The following method can be used, namely: first, the distribution power of the photovoltaic end, the charging power of the chemical energy storage end and the discharge power of the chemical energy storage end in the photoelectric storage capacity configuration at each moment are obtained from the configuration information of the photoelectric storage capacity in different time periods, and the standard deviations of the distribution power of the photovoltaic end, the charging power of the chemical energy storage end and the discharge power of the chemical energy storage end are calculated respectively, and then the average value of the three standard deviations is used as the standard deviation of the configured power when the photoelectric storage capacity is configured; secondly, the standard deviation of the configured power when the photoelectric storage capacity is configured is calculated from the configuration information of the photoelectric storage capacity in different time periods. The maximum distribution power of the photovoltaic end and the minimum distribution power of the photovoltaic end are obtained from the configuration information of the light storage capacity of each time period, and the difference between the maximum distribution power and the minimum distribution power is used as the power variability of the photovoltaic end. Then, the maximum discharge power of the chemical energy storage end and the minimum discharge power of the chemical energy storage end are obtained from the configuration information of the light storage capacity of each time period, and the difference between the maximum discharge power and the minimum discharge power is used as the power variability of the chemical energy storage end. Finally, the sum of the power variability of the photovoltaic end and the power variability of the chemical energy storage end is used as the variability of the configuration power.

[0093] It should be noted that the standard deviation of the configured power in the present application reflects the volatility of the configured power of the optical storage capacity; the variability of the configured power reflects the degree of change of the configured power of the optical storage capacity, and the greater the variability, the greater the degree of change of the configured power of the optical storage capacity, and the smaller the variability, the smaller the degree of change of the configured power of the optical storage capacity.

[0094] In specific implementation, determining the trend factor of the configuration power change when the photo-storage capacity is configured can be achieved in the following manner, namely: obtaining the distribution power of the photovoltaic end and the discharge power of the chemical energy storage end when the photo-storage capacity is configured, performing linear fitting on the distribution power of the photovoltaic end and the discharge power of the chemical energy storage end respectively, to obtain a photovoltaic distribution power curve and a chemical energy storage discharge power curve, and then using the average slope of the photovoltaic distribution power curve and the chemical energy storage discharge power curve as the trend factor of the configuration power change when the photo-storage capacity is configured. It should be noted that the trend factor in the present application is an indicator for measuring the power change trend when the photo-storage capacity is configured, and the trend factor can timely reflect the characteristics of the distribution power changes of the photovoltaic end and the chemical energy storage end in the photo-storage capacity configuration in different time periods.

[0095] In specific implementation, the capacity compensation cost of the configuration unit in the target energy management and control platform when configuring the photovoltaic storage capacity in different time periods is determined based on the standard deviation of the configured power, the variability of the configured power, the trend factor and all power imbalances. This can be achieved in the following way, namely: select the power imbalance of a time period, initialize a cost model, use the standard deviation of the configured power, the variability of the configured power and the trend factor as constraint parameters of the cost model, use the power imbalance of the time period as the initialization parameter of the cost model, and then output the capacity compensation cost of the configuration unit in the target energy management and control platform when configuring the photovoltaic storage capacity in the time period through the cost model, repeat the above steps to obtain the capacity compensation cost when configuring the photovoltaic storage capacity in the remaining time periods.

[0096] It should be noted that the cost model in the present application can use machine learning algorithms (such as regression algorithms, neural networks) to establish a cost relationship for photovoltaic storage capacity configuration. The cost relationship is, for example: capacity compensation cost = A*standard deviation of configured power + B*variability of configured power + C*trend factor + power imbalance, where A, B, and C are weight coefficients. A, B, and C can be determined based on a large amount of historical photovoltaic storage capacity configuration information, which will not be repeated here.

[0097] In addition, it should be noted that the capacity compensation cost in this application refers to the cost required to adjust the photovoltaic and energy storage capacity in the energy management system due to the power imbalance caused by changes in load demand and fluctuations in the output of renewable energy (such as photovoltaic and chemical energy storage systems) in order to maintain stable and reliable operation of the system. The cost of the photovoltaic and energy storage capacity configuration can be quantified through the capacity compensation cost, which can effectively respond to changes in load demand and fluctuations in renewable energy and improve the stability and reliability of the system.

[0098] In step 104, the energy allocation amount of the target energy management and control platform is predicted based on the historical energy allocation data to obtain the energy allocation prediction amount, and then the energy allocation prediction amount is adjusted by the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value.

[0099] In some embodiments, the energy allocation amount of the target energy management and control platform is predicted based on the historical energy allocation data, and the energy allocation prediction amount can be obtained by the following steps:

[0100] Train the energy allocation prediction model by using a specified amount of historical energy allocation data;

[0101] Using current energy allocation data as input parameters of the energy allocation prediction model;

[0102] Then, the energy allocation prediction model is used to output the energy allocation prediction amount.

[0103] In specific implementation, training the energy allocation prediction model by using a specified number of historical energy allocation data can be achieved in the following manner, namely: the specified number of historical energy allocation data refers to a large amount of historical energy allocation data, and the data volume is more than 10,000, and then the machine learning algorithm model in the prior art is trained by using the specified number of historical energy allocation data to obtain the energy allocation prediction model. It should be noted that in the present application, the neural network model in the prior art can also be trained by using the specified number of historical energy allocation data to obtain the energy allocation prediction model, which is not limited here. In addition, the energy allocation prediction amount in the present application represents the predicted value of the energy allocation amount, wherein the energy allocation prediction amount includes: the distribution power prediction amount of the photovoltaic end, the charging power prediction amount of the chemical energy storage end, the discharge power prediction amount of the chemical energy storage end and the compensation power prediction amount of the supercapacitor; it should also be noted that the current energy allocation data in the present application refers to the energy allocation data corresponding to the current moment to the past half hour.

[0104] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the energy forecast adjustment value in some embodiments of the present application. In this embodiment, the energy allocation forecast amount is adjusted by the power fluctuation entropy and all capacity compensation costs, and the energy forecast adjustment value can be obtained by the following steps:

[0105] First, in step 1041, the time period information corresponding to the energy allocation forecast amount is obtained;

[0106] Then, in step 1042, the capacity compensation price corresponding to the energy allocation forecast amount is screened out from all the capacity compensation prices according to the time period information, and the capacity compensation price is used as the target capacity compensation price;

[0107] Finally, in step 1043, the energy allocation forecast value is adjusted according to the target capacity compensation cost and the power fluctuation entropy to obtain an energy forecast adjustment value.

[0108] In specific implementation, obtaining the time period information corresponding to the energy allocation forecast amount can be achieved in the following manner, namely: obtaining the timestamp of the energy allocation forecast amount, determining in which time period the timestamp is located (the time periods include: morning rising period (6 am to 10 am), noon peak period (10 am to 3 pm), afternoon falling period (3 pm to 6 pm), night quiet period (6 pm to 6 am the next day)), and using the time period in which the timestamp is located as the time period information corresponding to the energy allocation forecast amount; filtering out the capacity compensation cost corresponding to the energy allocation forecast amount from all capacity compensation costs through the time period information can be achieved in the following manner, namely: using the capacity compensation cost corresponding to the time period information as the capacity compensation cost corresponding to the energy allocation forecast amount.

[0109] In specific implementation, the energy allocation prediction amount is adjusted according to the target capacity compensation cost and the power fluctuation entropy, and the energy prediction adjustment value can be obtained in the following manner, namely: first, the power fluctuation entropy is used as the adjustment coefficient of the energy allocation prediction amount, and then the energy allocation prediction amount is adjusted by the adjustment coefficient to obtain the allocation adjustment value. As a preferred embodiment, the natural exponential of the opposite number of the power fluctuation entropy can be used as the adjustment coefficient, and the allocation adjustment value can be determined by the following formula, namely: allocation adjustment value = adjustment coefficient * energy allocation prediction amount; secondly, the allocation adjustment value is cost-compensated by the target capacity compensation cost, and then the allocation adjustment value after cost compensation is used as the energy allocation prediction adjustment value. As a preferred embodiment, the energy allocation prediction adjustment value can be determined by the following formula, namely: energy allocation prediction adjustment value = allocation adjustment value + target capacity compensation cost. In other embodiments, the weighted sum of the allocation adjustment value and the target capacity compensation cost can also be used as the energy allocation prediction adjustment value, which is not limited here.

[0110] It should be noted that in the present application, the energy allocation forecast is adjusted by the adjustment coefficient, which is beneficial to reducing the impact of instability in the distribution system caused by transient fluctuations in electricity during energy switching; cost compensation of the allocation adjustment value by the target capacity compensation cost is beneficial to reducing the impact of the additional power consumed during the configuration of the photovoltaic storage capacity on the stability of the distribution system.

[0111] In addition, it should be noted that the energy prediction adjustment value in the present application refers to the amount after adjusting the distribution power prediction amount of the photovoltaic end, the charging power prediction amount of the chemical energy storage end, the discharge power prediction amount of the chemical energy storage end and the compensation power prediction amount of the supercapacitor in the energy allocation prediction amount. The energy prediction adjustment value includes: the distribution power prediction adjustment value of the photovoltaic end, the charging power prediction adjustment value of the chemical energy storage end, the discharge power prediction adjustment value of the chemical energy storage end and the compensation power prediction adjustment value of the supercapacitor.

[0112] In step 105, the photovoltaic storage capacity configuration of the configuration unit is adaptively regulated based on the energy prediction adjustment value.

[0113] In some embodiments, the adaptive regulation of the photovoltaic storage capacity configuration of the configuration unit based on the energy prediction adjustment value can be achieved by using the following steps:

[0114] Converting the energy forecast adjustment value into a configuration instruction of the light storage capacity;

[0115] The optical storage capacity configuration of the configuration unit is adjusted through the configuration instruction.

[0116] In specific implementation, the conversion of the energy prediction and adjustment value into the configuration instruction of the photoelectric storage capacity can be achieved in the following manner, namely: a list consisting of the distribution power prediction and adjustment value of the photovoltaic end, the charging power prediction and adjustment value of the chemical energy storage end, the discharge power prediction and adjustment value of the chemical energy storage end and the compensation power prediction and adjustment value of the supercapacitor in the energy prediction and adjustment value can be used as the configuration instruction of the photoelectric storage capacity.

[0117] In addition, in specific implementation, adjusting the optical storage capacity configuration of the configuration unit through the configuration instruction can be implemented in the following manner, that is: the configuration instruction of the optical storage capacity can be used as the execution instruction of the optical storage capacity configuration at the next moment.

[0118] On the other hand, in some embodiments, the present application provides an energy management and control platform system, referring to Figure 4 , which is a schematic diagram of exemplary hardware and / or software of an energy management and control platform system according to some embodiments of the present application, the energy management and control platform system 400 includes: an acquisition module 401, a processing module 402 and an execution module 403, which are described as follows:

[0119] Acquisition module 401, in this application, acquisition module 401 is mainly used to collect energy allocation information of the target energy management and control platform, and then obtain historical energy allocation data;

[0120] Processing module 402, in the present application, is used to extract the transient fluctuation amount of the power of the dispatching unit in the target energy management and control platform at each energy switch from the historical energy dispatching data, and then determine the power fluctuation entropy of the dispatching unit when performing energy switching based on the transient fluctuation amount of all power and the power distribution information under the stable power distribution state;

[0121] The processing module 402 in the present application is also used to obtain the configuration information of the optical storage capacity in different time periods, determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the optical storage capacity in each time period, and determine the capacity compensation cost when the configuration unit in the target energy management and control platform configures the optical storage capacity in different time periods through all the power imbalances and the change characteristics of the optical storage capacity configuration;

[0122] The processing module 402 in the present application is also used to predict the energy allocation amount of the target energy management and control platform according to the historical energy allocation data to obtain the energy allocation prediction amount, and then adjust the energy allocation prediction amount through the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value;

[0123] The execution module 403 in the present application is mainly used to adaptively control the photovoltaic storage capacity configuration of the configuration unit based on the energy prediction adjustment value.

[0124] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores a code, and the processor is configured to obtain the code and execute the control method of the above-mentioned energy management and control platform.

[0125] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device according to some embodiments of the present application using the control method of the energy management and control platform. The control method of the energy management and control platform in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0126] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0127] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0128] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0129] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The control method of the energy management and control platform in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0130] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0131] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0132] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0133] In addition, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the control method of the above-mentioned energy management and control platform.

[0134] Although the preferred embodiments of the present application have been described, those skilled in the art may make other 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 falling within the scope of the present application.

[0135] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A control method for an energy management and control platform, characterized in that: The steps include: Collect energy allocation information from the target energy management and control platform, and then obtain historical energy allocation data; Extracting the transient fluctuation amount of power of the dispatching unit in the target energy management and control platform at each energy switch from the historical energy dispatching data, and then determining the power fluctuation entropy of the dispatching unit when performing energy switching from the transient fluctuation amount of all power and the power distribution information under the stable power distribution state; Obtain the configuration information of the photovoltaic storage capacity in different time periods, determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the photovoltaic storage capacity in each time period, and determine the capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods through all the power imbalances and the change characteristics of the photovoltaic storage capacity configuration; Predicting the energy allocation amount of the target energy management and control platform according to the historical energy allocation data to obtain the energy allocation prediction amount, and then adjusting the energy allocation prediction amount according to the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value; Adaptively regulating the configuration of the light storage capacity of the configuration unit based on the energy prediction adjustment value; The power fluctuation entropy of the dispatching unit when performing energy switching is determined by the transient fluctuation amount of all power and the power distribution information under the stable power distribution state, specifically including: Determine the baseline fluctuation of energy in the energy allocation process through the power distribution information under the stable power distribution state; Determine the transient fluctuation variance according to the transient fluctuation amount of all electric power; Determining the power fluctuation entropy of the dispatching unit when performing energy switching according to the transient fluctuation amount of all power, the reference fluctuation amount and the transient fluctuation variance; Among them, the capacity compensation cost of configuring the photovoltaic storage capacity in different time periods by the configuration unit in the target energy management and control platform is determined by all the power imbalance and the change characteristics of the photovoltaic storage capacity configuration, which specifically includes: Obtain configuration information of optical storage capacity at different time periods; Determine the standard deviation of the configured power and the variability of the configured power when configuring the optical storage capacity through the configuration information of the optical storage capacity in different time periods; Determine a trend factor of configuration power change when configuring the optical storage capacity, and use the trend factor as a change feature of the optical storage capacity configuration; The capacity compensation cost when the configuration unit in the target energy management and control platform configures the photovoltaic storage capacity in different time periods is determined according to the standard deviation of the configured power, the variability of the configured power, the trend factor and all power imbalances.

2. The method according to claim 1, characterized in that Extracting the transient fluctuation amount of power of the dispatching unit in the target energy management and control platform at each energy switch from the historical energy dispatching data specifically includes: Determine a switching node for each energy switch according to the historical energy allocation data; Extracting energy allocation information before and after each switching node is switched from the historical energy allocation data; The transient fluctuation of power when the energy of the corresponding switching node is switched is determined through each energy allocation information.

3. The method according to claim 1, characterized in that Determining the power imbalance degree when the load changes in the corresponding period according to the configuration information of the photovoltaic storage capacity in each period specifically includes: Select a time period from each time period as a designated time period, and then obtain configuration information of the optical storage capacity of the designated time period; Obtaining the load demand power when the load changes during the specified time period; Extracting the load power when the load changes from the configuration information of the photovoltaic storage capacity during the specified time period; Determine the power imbalance degree when the load changes during the specified period of time by using the load demand power and the load power; Repeat the above steps to obtain the power imbalance degree when the load changes in the remaining time period.

4. The method according to claim 1, characterized in that The energy allocation amount of the target energy management and control platform is predicted based on the historical energy allocation data, and the energy allocation prediction amount obtained specifically includes: Train the energy allocation prediction model by using a specified amount of historical energy allocation data; Using current energy allocation data as input parameters of the energy allocation prediction model; Then, the energy allocation prediction model is used to output the energy allocation prediction amount.

5. The method according to claim 1, characterized in that The adaptive regulation of the photovoltaic storage capacity configuration of the configuration unit based on the energy prediction adjustment value specifically includes: Converting the energy forecast adjustment value into a configuration instruction of the light storage capacity; The optical storage capacity configuration of the configuration unit is adjusted through the configuration instruction.

6. An energy management and control platform system, which uses the method described in any one of claims 1 to 5 to control the energy management and control platform, characterized in that: The system includes: The acquisition module is used to collect energy allocation information of the target energy management and control platform, and then obtain historical energy allocation data; A processing module, used to extract the transient fluctuation amount of the power of the allocation unit in the target energy management and control platform at each energy switch from the historical energy allocation data, and then determine the power fluctuation entropy of the allocation unit when performing energy switching based on the transient fluctuation amount of all power and the power distribution information under the stable power distribution state; The processing module is further used to obtain configuration information of the optical storage capacity in different time periods, determine the power imbalance when the load changes in the corresponding time period according to the configuration information of the optical storage capacity in each time period, and determine the capacity compensation cost when the configuration unit in the target energy management and control platform configures the optical storage capacity in different time periods through all the power imbalances and the change characteristics of the optical storage capacity configuration; The processing module is further used to predict the energy allocation amount of the target energy management and control platform according to the historical energy allocation data to obtain the energy allocation prediction amount, and then adjust the energy allocation prediction amount through the power fluctuation entropy and all capacity compensation costs to obtain the energy prediction adjustment value; An execution module is used to adaptively control the light storage capacity configuration of the configuration unit based on the energy prediction adjustment value.

7. A computer device, comprising a memory and a processor, wherein the memory stores a code, characterized in that: The processor is configured to obtain the code and execute the control method of the energy management and control platform according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the control method of the energy management and control platform according to any one of claims 1 to 5 is implemented.

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