A charging and discharging intelligent scheduling method and system for emergency backup battery pack

Abstract

The present application relates to the field of artificial intelligence technology, and provides a method and system for intelligent scheduling of charging and discharging of emergency backup battery packs. The method includes: dividing the power supply area based on the power consumption characteristics of the equipment, and obtaining multiple sets of power-consuming equipment; mining historical power consumption data for each set, and constructing a power demand judgment channel; real-time collection of regional power supply and power consumption, and calculation of the power supply and demand difference; when the supply and demand difference is less than a preset value, activating the power demand judgment channel, and obtaining multiple power compensation sequences; collecting the power supply of the backup battery pack, combining the compensation sequence for optimization, and obtaining emergency compensation power supply decisions; performing decision safety certification, and performing emergency charging. The present application solves the technical problems of power supply and demand mismatch and inaccurate emergency power supply scheduling in the power supply area through refined management of power-consuming equipment, intelligent prediction of power demand, and intelligent scheduling of emergency power supply, so as to improve the reliability and stability of power supply in the power supply area.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, specifically to the field of intelligent control technology, and in particular to a charging and discharging intelligent scheduling method and system for an emergency backup battery pack. Background Art

[0002] With the continuous advancement of science and technology and the growing demand for electricity in society, the stability and reliability of power supply have become particularly important. Especially in some key areas, once the power supply is interrupted, serious consequences will occur. Therefore, as the last line of defense to ensure power supply, the performance and management level of the emergency backup battery pack are directly related to the stable operation of the entire system. However, the existing emergency backup battery packs do not divide the power supply area into detailed power equipment, resulting in extensive power supply management and difficulty in accurately meeting the actual needs of each power equipment.

[0003] In this context, an intelligent scheduling method and system for charging and discharging of emergency backup battery packs came into being, aiming to solve the technical problems of low power supply reliability and stability caused by mismatch between power supply and demand in the power supply area and inaccurate emergency power supply scheduling through refined management of power-consuming equipment, intelligent prediction of power demand and intelligent scheduling of emergency power supply. Summary of the invention

[0004] The present application provides a charging and discharging intelligent scheduling method and system for an emergency backup battery pack, aiming to solve the technical problems of low reliability and stability of power supply caused by mismatch between power supply and demand in the power supply area and inaccurate emergency power supply scheduling through refined management of power-consuming equipment, intelligent prediction of power demand and intelligent scheduling of emergency power supply.

[0005] In view of the above problems, the present application provides a method and system for intelligent scheduling of charging and discharging of an emergency backup battery pack.

[0006] In a first aspect disclosed in the present application, a method for intelligent scheduling of charging and discharging of an emergency standby battery pack is provided, the method comprising: determining a power supply area, dividing power-consuming equipment in the power supply area based on equipment power consumption characteristics, and obtaining a plurality of power-consuming equipment sets; mining historical power consumption data for the plurality of power-consuming equipment sets respectively, and constructing a power demand judgment channel, wherein the power demand judgment channel comprises a plurality of power demand judgment subnetworks, which correspond one to one with the plurality of power-consuming equipment sets; collecting regional power supply and regional power consumption in the power supply area in real time, and calculating the regional power supply and demand difference; when the regional power supply and demand difference is less than a first preset supply and demand difference, activating the power demand judgment channel, performing power demand judgment on the plurality of power-consuming equipment sets, and obtaining a plurality of power compensation sequences, wherein each power compensation sequence has a different sequence length, and each sequence length is determined according to the emergency duration; collecting the battery power supply of the emergency standby battery pack, optimizing the compensation power supply in combination with the plurality of power compensation sequences, and obtaining an emergency compensation power supply decision; performing security authentication on the emergency compensation power supply decision, and after the authentication is passed, performing emergency charging on the plurality of power-consuming equipment sets based on the emergency compensation power supply decision.

[0007] Another aspect disclosed in the present application provides an intelligent scheduling system for charging and discharging of emergency backup battery packs, the system comprising: an equipment division module, the equipment division module is used to determine the power supply area, divide the power-consuming equipment in the power supply area based on the power consumption characteristics of the equipment, and obtain multiple power-consuming equipment sets; a channel construction module, the channel construction module is used to mine the historical power consumption data of the multiple power-consuming equipment sets respectively, and construct a power demand judgment channel, wherein the power demand judgment channel includes multiple power demand judgment sub-networks, which correspond one to one with the multiple power-consuming equipment sets; a supply and demand difference calculation module, the supply and demand difference calculation module is used to collect the regional power supply and regional power consumption of the power supply area in real time, and calculate the regional power supply and demand difference; demand A judgment module, wherein the demand judgment module is used to activate the power demand judgment channel when the power supply and demand difference in the area is less than a first preset power supply and demand difference, perform power demand judgment on a plurality of power equipment sets, and obtain a plurality of power compensation sequences, wherein each power compensation sequence has a different sequence length and each sequence length is determined according to the emergency duration; a power supply optimization module, wherein the power supply optimization module is used to collect the battery power supply of the emergency backup battery pack, perform compensation power supply optimization in combination with the plurality of power compensation sequences, and obtain an emergency compensation power supply decision; an emergency charging module, wherein the emergency charging module is used to perform security authentication on the emergency compensation power supply decision, and after the authentication is passed, perform emergency charging on the plurality of power equipment sets based on the emergency compensation power supply decision.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The above-mentioned intelligent charging and discharging scheduling method for emergency standby battery packs first defines the power supply area, and divides the power-consuming equipment in the power supply area into several sets according to the power consumption characteristics of different equipment. Subsequently, by deeply mining the historical power consumption data of these equipment sets, an accurate and efficient power demand judgment channel is constructed. After that, the power supply and power consumption of the power supply area are monitored in real time, and the power supply and demand difference is accurately calculated. Once the supply and demand difference is lower than the preset safety threshold, the power demand judgment channel will be activated, the power demand of each equipment set will be accurately evaluated, and the corresponding power compensation sequence will be generated. The length of these sequences is set according to the expected emergency duration to ensure that sufficient and reasonable power support can be provided in an emergency. At the same time, the power supply capacity data of the emergency standby battery pack is collected in real time, and the power supply scheme is optimized and selected in combination with the power compensation sequence to form a scientific and reasonable emergency compensation power supply decision. Then, the formed decision is safety certified. After the certification is passed, according to this decision, targeted emergency charging measures are implemented for each equipment set to ensure that power supply can be quickly restored in an emergency and the reliability and stability of power supply are guaranteed.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a flow chart of a method for intelligently scheduling charging and discharging of an emergency backup battery pack in one embodiment;

[0013] Figure 2 The present invention is an architecture diagram of a charging and discharging intelligent scheduling system for an emergency backup battery pack in one embodiment.

[0014] Explanation of the accompanying drawings: equipment division module 1, channel construction module 2, supply-demand difference calculation module 3, demand judgment module 4, power supply optimization module 5, emergency charging module 6. DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a method and system for intelligent scheduling of charging and discharging of an emergency backup battery pack to solve the technical problem of low reliability and stability of power supply caused by mismatch between power supply and demand in the power supply area and inaccurate emergency power supply scheduling.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides a method for intelligent scheduling of charging and discharging of an emergency backup battery pack, the method comprising:

[0020] Determine a power supply area, divide the power-consuming devices in the power supply area based on the power consumption characteristics of the devices, and obtain multiple power-consuming device sets;

[0021] With the continuous growth of electricity demand and the increasing complexity of the power supply system, the traditional power supply management method can no longer meet the requirements of modern society for the efficiency, stability and safety of power supply. In particular, in the face of emergencies such as sudden incidents and natural disasters, the reliability and continuity of power supply are severely challenged.

[0022] In the embodiment of the present application, after the system terminal determines the power supply area based on the power outage range, it performs detailed classification according to the power consumption characteristics of each device. These characteristics include the power, power consumption, operating time, etc. of the device. Through such division, the system terminal divides the power-consuming devices in the power supply area into multiple sets, and the devices in each set have similar power consumption characteristics. Such division helps the system terminal to manage power resources more accurately, meet the power consumption needs of different types of equipment, and improve the efficiency and reliability of power supply.

[0023] Performing historical power consumption data mining on the multiple power consumption equipment sets respectively to construct a power consumption demand judgment channel, wherein the power consumption demand judgment channel includes multiple power consumption demand judgment sub-networks corresponding to the multiple power consumption equipment sets one by one;

[0024] In one embodiment, for the divided multiple power consumption equipment sets, the system terminal further carries out the mining of historical power consumption data. By collecting the historical power consumption data of each set, and conducting in-depth analysis and learning, a power demand judgment subnetwork corresponding to each set is generated. Then each generated power demand judgment subnetwork is merged in parallel to construct a power demand judgment channel. Each subnetwork in this channel corresponds to a specific power consumption equipment set. These subnetworks can predict and judge the power demand of each equipment set based on the input data, realize intelligent prediction of power demand, and ensure the balance of power supply and demand.

[0025] Furthermore, the present application provides for mining historical power consumption data of the plurality of power consumption equipment sets respectively to construct a power consumption demand judgment channel, and the method further includes:

[0026] Traversing the multiple sets of electric devices and extracting a first set of electric devices;

[0027] Collect historical power consumption data of the first power-consuming equipment set to obtain a first power consumption scenario sample set and a first power demand sample set, wherein the power consumption scenario samples include emergency duration samples;

[0028] Performing supervised learning based on the first power usage scenario sample set and the first power usage demand sample set to generate a first power usage demand judgment subnetwork;

[0029] Preferably, the system terminal traverses all sets of power-consuming devices and randomly selects a set as the target of current processing, namely, the first set of power-consuming devices. Subsequently, the system terminal collects the historical power consumption data of this set, which includes data under different power consumption scenarios and power demand data under the scenario, namely, the first power consumption scenario sample set and the first power demand sample set. Among them, the power consumption scenario sample set contains emergency duration samples. After obtaining these data, the system terminal uses the supervised learning method for processing. Specifically, the system terminal uses the first power consumption scenario sample set as the input feature of the neural network and the corresponding first power demand sample set as the output label of the neural network. These data are then preprocessed to remove existing outliers and use adjacent data to supplement missing values ​​to ensure the accuracy and completeness of the data. Subsequently, the system terminal determines the number of neurons in the input layer according to the number of input features, namely, the type of data in the first power consumption scenario sample set. Then, the number of hidden layers, the number of neurons in each layer, and the activation function are determined according to the complexity of the actual situation and the size of the input power consumption scenario sample set. For example, if only the power demand of a certain device is judged, and there are few input features, a hidden layer and ReLU can be selected to build a neural network. Here, ReLU can alleviate the gradient vanishing problem and accelerate the training process. After that, the number of neurons in the output layer and the corresponding activation function or loss function are determined according to the number of power demands to be output. For example, if three types of power demands, high, medium, and low, need to be output, the number of neurons in the output layer should be the same as the number of categories, and the softmax activation function or cross entropy loss function should be used, or if only one demand value is output, only one neuron needs to be set in the output layer, and a linear activation function or a regression-related loss function should be used. After the basic framework of the power demand judgment network is constructed, the system terminal trains the network according to the power scene samples and power demand samples of the first power-consuming device set to generate a special sub-network, namely the first power demand judgment sub-network. This sub-network is specifically used to predict the power demand of the device set in a specific scenario. In summary, the above process uses historical data for training to generate a sub-network that can predict the power demand of a specific device set in a specific scenario. In this way, when it is necessary to predict the power demand of the device set in a certain scenario in the future, this sub-network can be used for prediction, so as to more accurately schedule and manage power resources.

[0030] Constructing a network for the plurality of sets of electric devices in sequence to obtain a plurality of electric demand determination sub-networks;

[0031] The multiple power demand judgment sub-networks are merged in parallel to obtain a power demand judgment channel.

[0032] Preferably, the system terminal uses the same method as the above-mentioned construction of the first power demand judgment subnetwork to construct a network for the remaining multiple power-consuming device sets and generate corresponding power demand judgment subnetworks. These subnetworks are designed based on the specific input characteristics of the corresponding device set, with the purpose of being able to more accurately predict the power demand of the device set in a specific scenario. Subsequently, the system terminal connects these subnetworks in parallel to construct a power demand judgment channel, and adds a routing matching mechanism at the entrance of the channel. When the input data is channeled, the routing matching mechanism will intercept the data flow, analyze the data flow, extract information directly related to the device set or the working environment, and then match the power demand judgment subnetwork that can receive this data based on the extracted information. In this way, the system terminal can consider the power demand of multiple device sets at the same time and obtain the demand judgment of each device set. In summary, by integrating multiple power demand judgment subnetworks into one channel, the speed of judging power demand can be accelerated, and more accurate judgment results can be generated, providing more comprehensive and accurate information for power resource scheduling.

[0033] Collect the regional power supply and regional power consumption of the power supply area in real time, and calculate the regional power supply and demand difference;

[0034] In one embodiment, the system terminal collects the power supply and power consumption of the power supply area in real time, which is a key step in the dispatching and management of power resources. Among them, the power supply refers to the total amount of power actually provided by the power grid in the area, and the power consumption refers to the total amount of power actually consumed by all users in the area. After obtaining these two data, the system terminal calculates the difference between the power supply and the power consumption to obtain the regional power supply and demand difference. This supply and demand difference can intuitively reflect the balance between the power supply and demand in the current power supply area. If the supply and demand difference is positive, it means that the power supply is greater than the power consumption, and the power supply is in excess; if the supply and demand difference is negative, it means that the power consumption is greater than the power supply, and the power supply is insufficient. Such real-time calculation and analysis can help the system terminal to promptly discover the imbalance between power supply and demand, so as to reasonably allocate power resources, ensure the stable and reliable power supply in the power supply area, and avoid power shortages or waste.

[0035] When the regional power supply-demand difference is less than the first preset supply-demand difference, the power demand judgment channel is activated to judge the power demand of multiple power-consuming equipment sets, and multiple power compensation sequences are obtained, wherein each power compensation sequence has a different sequence length, and each sequence length is determined according to the emergency duration;

[0036] In one embodiment, in order to cope with the situation of tight regional power supply and demand, it is necessary to accurately predict the demand of multiple power-consuming equipment sets and generate power compensation schemes with different sequence lengths. The power compensation sequence refers to the compensation measures generated based on these judgment results. The system terminal pre-sets a threshold, namely the first preset supply and demand difference, for judging the calculated power supply and demand. When the power supply and demand difference of the power supply area is lower than the first preset supply and demand difference, it indicates that the power supply in the area begins to become tight and cannot meet the normal needs of all power-consuming equipment, such as sudden power outages. In this case, the system terminal will respond quickly, automatically activate the power demand judgment channel, and collect power scenario data sets. When the power scenario data sets are input into the channel, the routing matching mechanism judges these data, and distributes them to the corresponding power demand judgment subnetwork according to the judgment results, and performs detailed analysis and judgment on these power-consuming equipment sets to predict their power demand in different scenarios. After the power demand judgment is completed, the system terminal calculates the difference between each predicted power demand and the corresponding current power supply, and obtains multiple power compensation sequences. These sequences are designed to make up for the difference between the current power supply and the power demand of power-consuming equipment, ensuring that the power-consuming equipment can operate normally or at least maintain key functions. Each power consumption compensation sequence has a different sequence length, which refers to the duration of the compensation measures, which is formulated according to the expected power shortage time and severity, that is, determined by the emergency duration. In this way, the system terminal can respond quickly and effectively in the case of tight power supply, ensuring that key equipment receives the necessary power support, thereby maintaining the normal operation of the entire power supply area.

[0037] Furthermore, the present application provides activating the power demand judgment channel, performing power demand judgment on a plurality of power-consuming equipment sets, and obtaining a plurality of power compensation sequences, and the method further includes:

[0038] Collect multiple real-time power consumption scene data of multiple power consumption equipment sets, input them into the power consumption demand judgment channel, and obtain multiple power consumption demand sequences;

[0039] Extracting power supply sequences of a plurality of power-consuming equipment sets to obtain power supply sequences of a plurality of equipments;

[0040] Based on the multiple power demand sequences and the multiple equipment power supply sequences, multiple power compensation sequences are generated.

[0041] Preferably, in order to accurately grasp the power situation in the power supply area, the system terminal first collects the power consumption scenario data of multiple power consumption equipment sets in real time. These real-time data are input into the power demand judgment channel, and multiple power demand sequences are obtained by the same method as above. These sequences show in detail the power demand of different equipment sets in specific scenarios. At the same time, the system terminal extracts the current power supply sequence for multiple power consumption equipment sets, that is, the existing power supply plan and the actual power supply situation, thereby obtaining multiple equipment power supply sequences. These power supply sequences reflect the operating status and power supply capacity of the current power supply system. Subsequently, the system terminal calculates the difference between the power demand sequence and the equipment power supply sequence. By calculating the difference between the two, it can be clearly understood which equipment sets have insufficient power supply and the degree of insufficient power supply. Based on the results of these difference calculations, multiple power compensation sequences are generated. These compensation sequences are designed for insufficient power supply, and are intended to ensure the power balance and stable operation of the power supply area through reasonable power allocation and compensation measures.

[0042] Collect the battery power supply of the emergency backup battery group, combine the multiple power consumption compensation sequences to optimize the compensation power supply, and obtain the emergency compensation power supply decision;

[0043] In one embodiment, in order to cope with the power shortage in the power supply area, the system terminal collects the battery power supply data of the emergency backup battery pack. These data reflect the power support capability that the backup battery pack can provide. Subsequently, the system terminal sums the multiple power compensation sequences obtained previously, and compares the calculation results with the battery power supply. When the sum of the compensation power supply is less than or equal to the battery power supply, the system terminal sets constraints and optimizes and adjusts the multiple power compensation sequences according to the set constraints to obtain an emergency compensation power supply decision. On the contrary, when the sum of the compensation power supply is greater than the battery power supply, the system terminal downgrades the power compensation sequence of multiple power-consuming devices on the premise of maintaining key functions, reduces the compensation power, and ensures that the sum of the compensation power supply is within the range of the battery power supply. When the compensation power supply is adjusted to less than or equal to the battery power supply, the same operation is performed again to obtain the emergency compensation power supply decision. In summary, through optimal calculation, the system terminal finally obtains the emergency compensation power supply decision. This decision is intended to meet the power demand of power-consuming devices to the greatest extent while ensuring the efficiency and sustainability of emergency power supply.

[0044] Further, the present application provides collecting the battery power supply of the emergency backup battery group, combining the multiple power consumption compensation sequences to optimize the compensation power supply, and obtaining the emergency compensation power supply decision, and the method also includes:

[0045] Counting the compensation power quantities at all times in the multiple power consumption compensation sequences to obtain the sum of the compensation power quantities;

[0046] When the sum of the compensation power supply is less than or equal to the battery power supply, a compensation power supply constraint is set:

[0047] A: The compensation power supply of each set of electrical equipment at each moment is non-negative and does not exceed the corresponding power compensation amount;

[0048] B: The total compensation power supply power at each moment does not exceed the maximum discharge power of the emergency backup battery pack;

[0049] Preferably, in order to more accurately evaluate whether the power supply capacity of the emergency backup battery group meets the current power compensation demand, the system terminal counts the compensation power at each moment in all power compensation sequences, and sums them to obtain the compensation power supply and. After that, the system terminal compares the size relationship between the compensation power supply and the battery power supply. If the compensation power supply and is less than or equal to the battery power supply, it means that the backup battery group is capable of meeting the current compensation power supply demand. In this case, in order to ensure the effectiveness and safety of power supply, the system terminal sets two compensation power supply constraints. The first constraint ensures that at each moment, the compensation power supply of each power-consuming device set is non-negative and does not exceed its corresponding power compensation. This means that during emergency power supply, for any moment and any device set, the compensation power supply provided by the system terminal must be greater than or equal to 0 and cannot be a negative number, that is, during emergency power supply, it can only output power to the device set, but cannot absorb power from the device set. It will not provide power that exceeds the actual power shortage, avoiding unnecessary waste. The second constraint is to limit the total compensation power supply power at each moment to not exceed the maximum discharge power of the emergency backup battery group. This is to prevent the system terminals from being damaged or affecting the service life of the equipment due to excessive discharge, and to ensure that the battery pack can provide power stably and safely.

[0050] Based on the compensation power supply constraint, the multiple electricity consumption compensation sequences are optimized and adjusted to obtain an emergency compensation power supply decision.

[0051] Preferably, after determining the compensation power supply constraint, the system terminal checks whether each power consumption compensation sequence satisfies the two compensation power supply constraints set previously. Verify whether the compensation power supply of each set of power consumption equipment at each moment is non-negative and does not exceed the corresponding power consumption compensation amount, and check whether the total compensation power supply power at each moment does not exceed the maximum discharge power of the emergency standby battery group. When a sequence that does not meet the constraint conditions appears, the system terminal sets the decision variable, that is, the compensation power supply of each set of power consumption equipment at each moment. Determine the objective function, that is, the total compensation power supply that maximizes the power demand. By determining the variables and the objective function, a linear programming model is constructed, and the two established constraints are integrated in the model. Subsequently, the power consumption compensation sequence that does not meet the constraint conditions is input into the linear programming model, and the decision variables are assigned initial values. Then call the linear programming solver to solve the linear programming problem. The solver will find the optimal solution through iterative calculation according to the objective function and the constraint conditions, and output the optimal solution to the system terminal. When the system terminal receives the power consumption compensation sequence transmitted by the solver, the optimized power consumption compensation sequence is tested to determine whether all constraints are met. If it is satisfied, the optimal solution is used as the redistributed compensation power supply to obtain the emergency compensation power supply decision. If it is not satisfied, the system terminal adjusts the objective function in the linear programming model and then solves it again. For example, a weight factor is introduced to assign weights according to factors such as the importance of the equipment and the urgency of the power demand. Finally, the system terminal makes a fine adjustment to the compensation power supply in the power compensation sequence through the above iterative optimization process, so that it is as close as possible to the actual needs of the power equipment while meeting the constraints, thereby maximizing the power supply efficiency. After optimization and adjustment, the final emergency compensation power supply decision is obtained. This decision not only meets the needs of power compensation, but also ensures the safety and stability of the power supply process, providing an effective solution for coping with power shortages.

[0052] Further, the present application provides the compensatory power supply and the power supply amount greater than the battery power supply, and the method further includes:

[0053] When the sum of the compensation power supply is greater than the battery power supply, extracting the primary power consumption adjustment coefficients of the plurality of power consumption equipment sets respectively;

[0054] Based on the first-level electricity consumption adjustment coefficient, the plurality of electricity consumption compensation sequences are compensated and adjusted to obtain a plurality of first-level updated electricity consumption compensation sequences;

[0055] Optionally, when the compensation power supply exceeds the battery power supply, this means that the power that the system terminal can provide is insufficient to meet all current power demands. In this case, the system terminal prioritizes the power consumption set to determine which devices should be given priority in power supply. First, the primary power consumption adjustment coefficients of multiple power consumption sets are extracted. These coefficients are 1 by default, representing the power requirements required by this power consumption set to meet normal operation. Subsequently, the system terminal evaluates the importance of multiple power consumption sets, considering factors such as their criticality, impact on the overall system, and urgency of power demand. The equipment sets are divided into two categories according to their importance, one for storing equipment sets with higher importance and the other for storing equipment sets with lower importance. For equipment sets with higher importance, their primary power consumption adjustment coefficients are kept unchanged and remain at 1. For equipment sets with lower importance, their primary power consumption adjustment coefficients are reduced step by step according to the evaluation results to reduce power demand while maintaining key functions. Afterwards, the system terminal recalculates the compensation power supply of each equipment set based on the adjusted primary power consumption adjustment coefficients. For the equipment set with reduced level adjustment coefficient, its compensatory power supply will be reduced accordingly to reflect its reduced power demand. Then, based on the multiple power compensation sequences after the compensation adjustment, multiple updated first-level updated power compensation sequences are generated. These sequences reflect the amount of compensatory power supply that each power equipment set should obtain when it is able to maintain key functions in the case of insufficient power. In summary, when the power reserve is insufficient, the first-level power adjustment coefficient is extracted to adjust the power compensation sequence to ensure that high-priority equipment can obtain more power supply, thereby optimizing the allocation of power resources.

[0056] Obtaining a sum of the primary update compensation power supply amounts based on the plurality of primary update power consumption compensation sequences;

[0057] When the sum of the first-level updated compensation power supply is less than or equal to the battery power supply, the multiple first-level updated power compensation sequences are optimized and adjusted to obtain an emergency compensation power supply decision.

[0058] Optionally, the system terminal calculates the sum of the first-level updated compensation power supplies based on multiple first-level updated power compensation sequences, which represents the total power required by each device set after priority adjustment. Afterwards, this total power supply is compared with the actual power supply of the battery. If the sum of the first-level updated compensation power supplies is less than or equal to the battery power supply, it means that the power that the system terminal can provide is sufficient to meet the power demand of all current device sets. In this case, the system terminal performs the same operations as above on multiple first-level updated power compensation sequences to obtain emergency compensation power supply decisions. In summary, when the battery power supply is sufficient, optimization and adjustment are performed based on the first-level updated power compensation sequence to obtain a more efficient and reasonable emergency compensation power supply decision, thereby ensuring the stable operation of the power system.

[0059] Further, the present application provides that when the updated compensation power supply is greater than the battery power supply, the secondary power consumption adjustment coefficients of the plurality of power consumption equipment sets are extracted respectively, and the method further includes:

[0060] Based on the secondary power consumption adjustment coefficient, the plurality of power consumption compensation sequences are compensated and adjusted to obtain a plurality of secondary updated power consumption compensation sequences;

[0061] The multiple secondary updated electricity compensation sequences are optimized and adjusted to obtain emergency compensation power supply decisions.

[0062] Optionally, when the system terminal compares the updated compensation power supply and the battery power supply, if the updated compensation power supply is still greater than the battery power supply, the system terminal performs a secondary adjustment to obtain a secondary power adjustment coefficient. Subsequently, based on the secondary power adjustment coefficient, further compensation adjustments are performed on multiple processed power compensation sequences according to the same steps to obtain multiple secondary updated power compensation sequences. These sequences more accurately reflect the power distribution that each device set should obtain in the event of a power shortage. Afterwards, the same optimization and adjustment process is performed to obtain an emergency compensation power supply decision.

[0063] The emergency compensation power supply decision is subjected to safety authentication, and after the authentication is passed, emergency charging is performed on the plurality of power-consuming devices based on the emergency compensation power supply decision.

[0064] In one embodiment, after obtaining the emergency compensation power supply decision, in order to ensure the accuracy and safety of the emergency compensation power supply decision, the system terminal combines the charging and discharging simulation model to perform security authentication on the emergency compensation power supply decision. If the authentication is successful, the system terminal performs emergency charging on multiple sets of power-consuming equipment based on the authenticated emergency compensation power supply decision. This process is to allocate power to different sets of equipment in a targeted manner based on the decision results obtained through compensation adjustment and optimization adjustment. By executing this decision, the system terminal can ensure that in the event of a power shortage, power is provided to those important sets of equipment to ensure their normal operation. At the same time, for those non-critical devices or functions, the system terminal will also make corresponding power allocations based on the decision results to maintain the operation of critical functions. In this way, not only can limited power resources be efficiently utilized, but also the stable operation of the power system can be ensured, and the impact on equipment operation can be minimized.

[0065] Further, the present application provides that the regional electricity supply and demand difference is greater than the second preset supply and demand difference, and the method also includes:

[0066] When the regional power supply and demand difference is greater than a second preset supply and demand difference, extracting a power supply increment sequence;

[0067] Collecting the remaining battery capacity of the emergency backup battery pack, and generating a battery pack charging strategy based on the remaining battery capacity, the power supply increment sequence and the maximum charging power of the battery pack;

[0068] The emergency backup battery pack is charged and managed based on the battery pack charging strategy.

[0069] Preferably, the system terminal pre-sets a second preset supply-demand difference, and this preset value is used to determine whether the current power supply is in excess. When the system terminal compares the regional power supply-demand difference with the second preset supply-demand difference, if the comparison result is greater than the second preset supply-demand difference, this means that the current battery power supply exceeds the actual power demand of the region. In this case, the system terminal collects the remaining capacity of the battery as the basic data for the subsequent charging strategy formulation. Then extract the power supply increment sequence to reflect the changing trend of the current power supply and demand situation, especially the degree of power surplus, and analyze this sequence to determine the duration and change rate of power surplus. Subsequently, the maximum charging power of the battery pack is obtained to determine the maximum charging speed that the battery can accept, so as to avoid safety problems such as battery overheating and damage caused by excessive charging speed. Afterwards, the battery remaining capacity, the power supply increment sequence and the maximum charging power of the battery pack are comprehensively considered to formulate a battery pack charging strategy. The purpose of this strategy is not only to avoid power waste, but also to ensure the safety and long-term performance of the battery pack. For example, if the remaining capacity of the battery is moderate or low, and the power supply increment sequence shows that the power surplus will gradually decrease, then the charging strategy formulated is to charge quickly at the maximum charging power to make full use of the current power surplus conditions. Finally, the system terminal manages the charging or discharging of the emergency backup battery pack according to this management strategy. For example, if there is a lot of excess power, the system terminal will choose to discharge the battery pack to power other devices or store power for emergency use.

[0070] Further, the present application provides that the regional electricity supply and demand difference is greater than the second preset supply and demand difference, and the method also includes:

[0071] Constructing a charge-discharge simulation model based on the multiple power-consuming equipment sets and the emergency backup battery pack; extracting attribute information of the multiple power-consuming equipment sets and the emergency backup battery pack, and generating decision verification constraints based on the attribute information and the charge-discharge simulation model, wherein the decision verification constraints are operating constraints of the emergency backup battery pack and the multiple power-consuming equipment sets;

[0072] If the emergency compensation power supply decision meets the decision verification constraints, the authentication is passed.

[0073] Preferably, the system terminal analyzes the characteristics of multiple sets of power-consuming devices in detail, including key information such as the power demand, working mode, and charge and discharge compatibility of each device. At the same time, a comprehensive understanding of the emergency backup battery pack is also obtained, including key parameters such as battery capacity, charge and discharge rate, and safety. Subsequently, the system terminal simulates the circuit, energy conversion, and battery chemical process in a simulation environment based on this information to establish corresponding equipment and battery pack models. These models can simulate the power demand of the equipment and the charge and discharge behavior of the battery pack in real situations. After these models are established, the system terminal sets the charge and discharge simulation parameters. These parameters include charge and discharge current, voltage range, charge and discharge cycle, etc., which will directly affect the simulation results. The setting of these parameters is based on actual needs and the characteristics of the equipment and battery pack. Afterwards, the set parameters are combined with the equipment and battery pack models to construct a charge and discharge simulation model. This model is used to simulate how these devices and battery packs are charged and discharged in actual situations.

[0074] After building the charge and discharge simulation model, the system terminal extracts the key attributes of each power-consuming device, such as rated power, operating voltage, current demand, and operating mode. At the same time, the key attributes of the battery pack, such as the total capacity, maximum charge and discharge current, charge and discharge efficiency, and remaining power, are extracted. Subsequently, the extracted key attributes are integrated to form a comprehensive attribute data set. The attribute data set is then analyzed to identify key influencing factors and potential risk points. For example, whether the total power of the equipment exceeds the power supply capacity of the battery pack, whether the remaining power of the battery pack meets the emergency power supply requirements, etc. After that, the extracted attribute information is input into the constructed charge and discharge simulation model. The simulation model is then run to simulate the energy interaction process between multiple power-consuming device sets and emergency backup battery packs in different scenarios. Then, based on the operation results of the simulation model, the problems and limitations that the battery pack and power-consuming devices may encounter during operation are analyzed. For example, the minimum power limit of the battery pack, the maximum charging rate, and avoiding overcharging and over-discharging; the minimum power requirement of the power-consuming device, the working time limit, etc. Based on these analysis results, the system terminal generates a series of operating constraints, which specify the requirements that the battery pack and power-consuming devices must meet during operation. Then, the generated operating constraints are integrated into decision verification constraints, which are the basis for verifying the emergency compensation power supply decision. The decision verification constraints ensure that any proposed emergency power supply decision must meet the operating constraints of the battery pack and the power-consuming equipment to ensure the safe and stable operation of the battery pack.

[0075] After obtaining the decision verification constraints, the system terminal compares the charge and discharge parameters in the emergency compensation power supply decision with the generated decision verification constraints. The charge and discharge parameters include the charging rate, discharge rate, power level of the battery pack, and the power requirements and operating time of the power-consuming equipment. These parameters directly affect the operating status and performance of the battery pack and the power-consuming equipment. By comparing the charge and discharge parameters in the emergency compensation power supply decision with the decision verification constraints one by one, it can be determined whether the decision meets all the operating constraints. If all parameters are within the allowable range and no constraints are violated, the system terminal determines that the emergency compensation power supply decision is feasible and meets the operating requirements of the battery pack and the power-consuming equipment. Therefore, through the security authentication of the emergency compensation power supply decision, the safety and effectiveness of the emergency compensation power supply decision can be ensured. Only when the decision meets all decision verification constraints, the authentication will pass, thereby ensuring that the emergency backup battery pack can operate normally and stably at critical moments to meet the needs of power-consuming equipment.

[0076] In summary, the embodiments of the present application have at least the following technical effects:

[0077] The embodiment of the present application determines the power supply area, and divides the power consumption equipment in the area according to the power consumption characteristics of the equipment to form multiple power consumption equipment sets. Subsequently, by mining the historical power consumption data of these sets, a power demand judgment subnetwork corresponding to each set is constructed, thereby forming a power demand judgment channel. When the power supply and demand difference of the power supply area collected in real time is less than the preset first supply and demand difference, the power demand judgment channel is activated, the power demand judgment is performed on each power consumption equipment set, and the corresponding power compensation sequence is generated. The length of these sequences is determined according to the emergency duration to guide the subsequent compensation power supply. After that, the power supply of the emergency backup battery group is collected, and the compensation power supply is optimized in combination with the above-mentioned power compensation sequence. When the compensation power supply amount and the power supply amount are less than or equal to the battery power supply amount, the power compensation sequence is optimized and adjusted by setting the compensation power supply constraint, so as to obtain an emergency compensation power supply decision. If the compensation power supply amount and the power supply amount exceed the battery power supply amount, the compensation level is adjusted according to the different power level adjustment coefficients of the equipment set, and the optimization and adjustment are performed again until the power supply demand is met. In addition, when the difference between power supply and demand in the power supply area is greater than the second preset difference between power supply and demand, that is, when there is an oversupply of electricity, the power supply increment sequence will be extracted, and combined with the remaining capacity and maximum charging power of the battery pack, a battery pack charging strategy will be generated to achieve charging management of the emergency backup battery pack, ensure the continuity and stability of power supply, and meet the different needs of power-consuming equipment. These technical effects jointly solve the technical problems of power supply and demand mismatch and inaccurate emergency power supply scheduling in the power supply area, so as to improve the reliability and stability of power supply in the power supply area.

[0078] Embodiment 2

[0079] Based on the same inventive concept as the intelligent charging and discharging scheduling method for an emergency backup battery pack in the aforementioned embodiment, Figure 2 As shown, the present application provides a charging and discharging intelligent scheduling system for an emergency backup battery pack, the system comprising:

[0080] Equipment division module 1: the equipment division module 1 is used to determine the power supply area, divide the power-consuming equipment in the power supply area based on the power consumption characteristics of the equipment, and obtain multiple power-consuming equipment sets;

[0081] Channel construction module 2: the channel construction module 2 is used to mine the historical power consumption data of the multiple power consumption equipment sets respectively, and construct a power demand judgment channel, wherein the power demand judgment channel includes multiple power demand judgment sub-networks, which correspond one-to-one to the multiple power consumption equipment sets;

[0082] Supply-demand difference calculation module 3: The supply-demand difference calculation module 3 is used to collect the regional power supply and regional power consumption of the power supply area in real time, and calculate the regional power supply and demand difference;

[0083] Demand judgment module 4: the demand judgment module 4 is used to activate the power demand judgment channel when the regional power supply and demand difference is less than the first preset supply and demand difference, perform power demand judgment on a plurality of power-consuming equipment sets, and obtain a plurality of power compensation sequences, wherein each power compensation sequence has a different sequence length, and each sequence length is determined according to the emergency duration;

[0084] Power supply optimization module 5: The power supply optimization module 5 is used to collect the battery power supply of the emergency backup battery pack, and optimize the compensation power supply in combination with the multiple power consumption compensation sequences to obtain an emergency compensation power supply decision;

[0085] Emergency charging module 6: The emergency charging module 6 is used to perform safety authentication on the emergency compensation power supply decision. After authentication, emergency charging is performed on the plurality of electrical equipment sets based on the emergency compensation power supply decision.

[0086] Furthermore, the channel construction module 2 is used to execute the following method:

[0087] Traversing the multiple sets of electric devices and extracting a first set of electric devices;

[0088] Collect historical power consumption data of the first power-consuming equipment set to obtain a first power consumption scenario sample set and a first power demand sample set, wherein the power consumption scenario samples include emergency duration samples;

[0089] Performing supervised learning based on the first power usage scenario sample set and the first power usage demand sample set to generate a first power usage demand judgment subnetwork;

[0090] Constructing a network for the plurality of sets of electric devices in sequence to obtain a plurality of electric demand determination sub-networks;

[0091] The multiple power demand judgment sub-networks are merged in parallel to obtain a power demand judgment channel.

[0092] Furthermore, the demand determination module 4 is used to execute the following method:

[0093] Collect multiple real-time power consumption scene data of multiple power consumption equipment sets, input them into the power consumption demand judgment channel, and obtain multiple power consumption demand sequences;

[0094] Extracting power supply sequences of a plurality of power-consuming equipment sets to obtain power supply sequences of a plurality of equipments;

[0095] Based on the multiple power demand sequences and the multiple equipment power supply sequences, multiple power compensation sequences are generated.

[0096] Furthermore, the power supply optimization module 5 is used to execute the following method:

[0097] Counting the compensation power quantities at all times in the multiple power consumption compensation sequences to obtain the sum of the compensation power quantities;

[0098] When the sum of the compensation power supply is less than or equal to the battery power supply, a compensation power supply constraint is set:

[0099] A: The compensation power supply of each set of electrical equipment at each moment is non-negative and does not exceed the corresponding power compensation amount;

[0100] B: The total compensation power supply power at each moment does not exceed the maximum discharge power of the emergency backup battery pack;

[0101] Based on the compensation power supply constraint, the multiple electricity consumption compensation sequences are optimized and adjusted to obtain an emergency compensation power supply decision.

[0102] Furthermore, the power supply optimization module 5 is used to execute the following method:

[0103] When the sum of the compensation power supply is greater than the battery power supply, extracting the primary power consumption adjustment coefficients of the plurality of power consumption equipment sets respectively;

[0104] Based on the first-level electricity consumption adjustment coefficient, the plurality of electricity consumption compensation sequences are compensated and adjusted to obtain a plurality of first-level updated electricity consumption compensation sequences;

[0105] Obtaining a sum of the primary update compensation power supply amounts based on the plurality of primary update power consumption compensation sequences;

[0106] When the sum of the first-level updated compensation power supply is less than or equal to the battery power supply, the multiple first-level updated power compensation sequences are optimized and adjusted to obtain an emergency compensation power supply decision.

[0107] Furthermore, the power supply optimization module 5 is used to execute the following method:

[0108] Based on the secondary power consumption adjustment coefficient, the plurality of power consumption compensation sequences are compensated and adjusted to obtain a plurality of secondary updated power consumption compensation sequences;

[0109] The multiple secondary updated electricity compensation sequences are optimized and adjusted to obtain emergency compensation power supply decisions.

[0110] Furthermore, the emergency charging module 6 is used to execute the following method:

[0111] When the regional power supply and demand difference is greater than a second preset supply and demand difference, extracting a power supply increment sequence;

[0112] Collecting the remaining battery capacity of the emergency backup battery pack, and generating a battery pack charging strategy based on the remaining battery capacity, the power supply increment sequence and the maximum charging power of the battery pack;

[0113] The emergency backup battery pack is charged and managed based on the battery pack charging strategy.

[0114] Furthermore, the emergency charging module 6 is used to execute the following method:

[0115] Constructing a charge-discharge simulation model based on the multiple power-consuming equipment sets and the emergency backup battery pack; extracting attribute information of the multiple power-consuming equipment sets and the emergency backup battery pack, and generating decision verification constraints based on the attribute information and the charge-discharge simulation model, wherein the decision verification constraints are operating constraints of the emergency backup battery pack and the multiple power-consuming equipment sets;

[0116] If the emergency compensation power supply decision meets the decision verification constraints, the authentication is passed.

[0117] The emergency backup battery pack is charged and managed based on the battery pack charging strategy.

[0118] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0120] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for intelligently scheduling charging and discharging of an emergency backup battery pack, characterized in that: The method comprises: Determine a power supply area, divide the power-consuming devices in the power supply area based on the power consumption characteristics of the devices, and obtain multiple power-consuming device sets; Performing historical power consumption data mining on the multiple power consumption equipment sets respectively to construct a power consumption demand judgment channel, wherein the power consumption demand judgment channel includes multiple power consumption demand judgment sub-networks corresponding to the multiple power consumption equipment sets one by one; Collect the regional power supply and regional power consumption of the power supply area in real time, and calculate the regional power supply and demand difference; When the regional power supply-demand difference is less than the first preset supply-demand difference, the power demand judgment channel is activated to judge the power demand of multiple power-consuming equipment sets, and multiple power compensation sequences are obtained, wherein each power compensation sequence has a different sequence length, and each sequence length is determined according to the emergency duration; Collect the battery power supply of the emergency backup battery group, combine the multiple power consumption compensation sequences to optimize the compensation power supply, and obtain the emergency compensation power supply decision; Performing emergency charging on the plurality of electrical equipment sets based on the emergency compensation power supply decision; The battery power supply of the emergency backup battery pack is collected, and the compensation power supply optimization is performed in combination with the multiple power consumption compensation sequences to obtain an emergency compensation power supply decision, including: Counting the compensation power quantities at all times in the multiple power consumption compensation sequences to obtain the sum of the compensation power quantities; When the sum of the compensation power supply is less than or equal to the battery power supply, a compensation power supply constraint is set: A: The compensation power supply of each set of electrical equipment at each moment is non-negative and does not exceed the corresponding power compensation amount; B: The total compensation power supply power at each moment does not exceed the maximum discharge power of the emergency backup battery pack; Based on the compensation power supply constraint, optimizing and adjusting the multiple power consumption compensation sequences to obtain an emergency compensation power supply decision; The method further comprises: When the sum of the compensation power supply is greater than the battery power supply, extracting the primary power consumption adjustment coefficients of the plurality of power consumption equipment sets respectively; Based on the first-level electricity consumption adjustment coefficient, the plurality of electricity consumption compensation sequences are compensated and adjusted to obtain a plurality of first-level updated electricity consumption compensation sequences; Obtaining a sum of the primary update compensation power supply amounts based on the plurality of primary update power consumption compensation sequences; When the first-level updated compensation power supply amount is less than or equal to the battery power supply amount, optimizing and adjusting the multiple first-level updated power consumption compensation sequences to obtain an emergency compensation power supply decision; When the updated compensation power supply is greater than the battery power supply, extracting the secondary power consumption adjustment coefficients of the plurality of power consumption equipment sets respectively; Based on the secondary power consumption adjustment coefficient, the plurality of power consumption compensation sequences are compensated and adjusted to obtain a plurality of secondary updated power consumption compensation sequences; The multiple secondary updated electricity compensation sequences are optimized and adjusted to obtain emergency compensation power supply decisions.

2. The method according to claim 1, characterized in that Performing historical power consumption data mining on the plurality of power-consuming equipment sets respectively and building a power demand judgment channel includes: Traversing the multiple sets of electric devices and extracting a first set of electric devices; Collect historical power consumption data of the first power-consuming equipment set to obtain a first power consumption scenario sample set and a first power demand sample set, wherein the power consumption scenario samples include emergency duration samples; Performing supervised learning based on the first power usage scenario sample set and the first power usage demand sample set to generate a first power usage demand judgment subnetwork; Constructing a network for the plurality of sets of electric devices in sequence to obtain a plurality of electric demand determination sub-networks; The multiple power demand judgment sub-networks are merged in parallel to obtain a power demand judgment channel.

3. The method according to claim 1, characterized in that Activating the power demand judgment channel, judging the power demand of a plurality of power-consuming equipment sets, and obtaining a plurality of power compensation sequences, including: Collect multiple real-time power consumption scene data of multiple power consumption equipment sets, input them into the power demand judgment channel, and obtain multiple power demand sequences; Extracting power supply sequences of a plurality of power-consuming equipment sets to obtain power supply sequences of a plurality of equipments; Based on the multiple power demand sequences and the multiple equipment power supply sequences, multiple power compensation sequences are generated.

4. The method according to claim 1, characterized in that: The method further comprises: When the regional power supply and demand difference is greater than a second preset supply and demand difference, extracting a power supply increment sequence; Collecting the remaining battery capacity of the emergency backup battery pack, and generating a battery pack charging strategy based on the remaining battery capacity, the power supply increment sequence and the maximum charging power of the battery pack; The emergency backup battery pack is charged and managed based on the battery pack charging strategy.

5. An intelligent scheduling system for charging and discharging of emergency backup batteries, characterized in that: The system is used to execute the method according to any one of claims 1 to 4, and the system comprises: Equipment division module: determining a power supply area, dividing the power-consuming equipment in the power supply area based on the power consumption characteristics of the equipment, and obtaining a plurality of power-consuming equipment sets; A channel construction module: mining historical power consumption data of the plurality of power-consuming device sets respectively to construct a power demand judgment channel, wherein the power demand judgment channel includes a plurality of power demand judgment sub-networks corresponding to the plurality of power-consuming device sets one by one; Supply-demand difference calculation module: collects the regional power supply and regional power consumption of the power supply area in real time, and calculates the regional power supply-demand difference; Demand judgment module: when the regional power supply and demand difference is less than the first preset supply and demand difference, the power demand judgment channel is activated, the power demand judgment is performed on a plurality of power-consuming equipment sets, and a plurality of power compensation sequences are obtained, wherein each power compensation sequence has a different sequence length, and each sequence length is determined according to the emergency duration; Power supply optimization module: collects the battery power supply of the emergency backup battery group, combines the multiple power consumption compensation sequences to optimize the compensation power supply, and obtains the emergency compensation power supply decision; Emergency charging module: performs emergency charging on the plurality of electrical equipment sets based on the emergency compensation power supply decision.

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