Industrial and commercial energy storage system energy management method and system

Abstract

The invention provides an industrial and commercial energy storage system energy management method and system, and relates to the technical field of energy storage system energy management. The method comprises the steps of establishing an equipment power consumption feature library; according to the method, power utilization load information of a factory is monitored in real time, feature extraction is carried out to obtain a first feature, and then the first feature is matched with a feature library, so that equipment causing power utilization fluctuation and the running state of the equipment are accurately determined. On the basis, according to the typical power demand and duration of the equipment, a part of the current available power of the energy storage battery can be transferred as the emergency reserve power, so that instantaneous high-power fluctuation is effectively dealt with, and high-price electricity is prevented from being drawn from a power grid. Meanwhile, according to the method, the charging and discharging strategy of the energy storage system can be adaptively adjusted according to the operation state of the equipment, the actual performance of the energy storage battery and the service characteristics of the external power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management of energy storage systems, in particular to an energy management method and system for industrial and commercial energy storage systems. BACKGROUND

[0002] In modern industrial and commercial production, industrial and commercial energy storage systems are widely deployed to optimize electricity usage, reduce operating costs and improve energy utilization efficiency. These systems are usually equipped with energy management methods to develop charging and discharging strategies for the energy storage system based on time-of-use electricity prices, historical electricity consumption data and production plans, to achieve "peak clipping and valley filling" to save electricity costs. However, in actual operation, existing energy management methods face multiple challenges.

[0003] Firstly, the performance of energy storage batteries will deteriorate over time and with environmental factors such as high temperature, manifested as capacity reduction and internal resistance increase. Existing energy management strategies are often designed based on initial battery performance parameters and do not consider the continuous performance degradation of the battery in real time and in detail. This causes deviations between the actual available power, the available storage power and the charging and discharging efficiency of the battery and the expected performance when the energy storage system performs "peak clipping and valley filling" tasks, for example, the battery cannot provide enough power during peak electricity consumption or the battery cannot be fully charged during low consumption periods, thereby increasing the cost of high-priced electricity or missing the opportunity to store low-cost electricity.

[0004] Secondly, the electricity consumption load characteristics of the production line inside the factory are increasingly complex. For example, the introduction of new production lines can cause transient high-power fluctuations in electricity consumption load, and the fluctuation time point and duration have high randomness and uncertainty. The electricity consumption load prediction strategy of existing energy management systems is mainly based on relatively stable historical data, and the prediction accuracy is greatly reduced when dealing with such high-frequency, high-power and highly random electricity consumption shocks. This causes a serious disconnection between the charging and discharging plan of the energy storage system and the actual electricity demand of the factory, which can cause the energy storage system to pull high-priced electricity from the grid when electricity consumption surges or waste electricity when electricity consumption drops.

[0005] Furthermore, the external power grid environment has also become more dynamic and complex. For example, the introduction of real-time electricity pricing mechanisms and frequency regulation auxiliary service markets brings potential additional cost risks and income opportunities for industrial and commercial energy storage systems. Existing energy management methods mainly focus on fixed "peak clipping and valley filling" strategies and cannot effectively capture and respond to real-time electricity price fluctuations or participate in frequency regulation auxiliary services that require rapid and accurate power regulation. In the context of battery performance degradation, highly uncertain internal electricity consumption load and complex and dynamic external power grid environment, existing energy management methods cannot effectively adjust the charging and discharging strategy of the energy storage system, thereby failing to maximize the overall economic benefits of the energy storage system while ensuring the continuity of factory production and optimizing electricity costs.

[0006] The prior art needs to be improved in view of the above problems. SUMMARY

[0007] The present application discloses an industrial and commercial energy storage system energy management method and system, aiming to solve the problem that the existing industrial and commercial energy storage system energy management method is difficult to realize adaptive adjustment of the charging and discharging strategy of the energy storage system under the background of battery performance degradation, high uncertainty of internal power load, and complex and variable external power grid environment, thereby unable to maximize the overall economic benefit of the energy storage system on the basis of ensuring the continuity of factory production and optimizing power consumption cost.

[0008] The technical solution of the present application is as follows: In a first aspect, the present application discloses an industrial and commercial energy storage system energy management method, which comprises: establishing a device power consumption feature library; the feature library includes power consumption features of a specific device under different operating states; the specific device is a production device capable of generating instantaneous high-power fluctuations; real-time monitoring of factory power load information, feature extraction of the power load information to obtain a first feature, matching the first feature with the feature library to determine a first device and a first device operating state; according to the typical power demand and duration of the first device recorded in the feature library, a portion of the current available power of the energy storage battery is allocated as an emergency reserve power; at the same time, according to the first device operating state, the actual performance of the energy storage battery, and the external power grid service characteristics, the charging and discharging strategy of the energy storage system is adjusted.

[0009] Further, according to the above method, the power consumption features include instantaneous power curve, power change rate, current waveform feature, and state timing time; the method comprises: storing the power consumption features in the feature library in a structured data form; the structured data form includes specific device ID, operating state, and power consumption features.

[0010] In some preferred embodiments, the energy storage system comprises a power conversion system; the external power grid service characteristics include real-time price and response requirement; adjusting the charging and discharging strategy of the energy storage system according to the first device operating state, the actual performance of the energy storage battery, and the external power grid service characteristics comprises: evaluating the expected power demand and duration of the internal load peak corresponding to the first device operating state, and querying the current external power grid service characteristics; calculating the electricity cost saved by covering the internal load peak according to the expected power demand and duration of the internal load peak; calculating the expected income brought by participating in external auxiliary services according to the real-time price and response requirement; comparing the saved electricity cost with the expected income to obtain a benefit trade-off result; determining the scheduling priority of the energy storage system power according to the influence degree of the first device operating state on production; obtaining the actual available power and energy of the energy storage battery, and the maximum output capacity and temperature corresponding to the power conversion system; According to the scheduling priority, the benefit trade-off result, the actual available power and energy, the maximum output capability and temperature, the charging and discharging strategy of the energy storage system is adjusted.

[0011] The application further proposes that, according to the scheduling priority, the benefit trade-off result, the actual available power and energy, the maximum output capability and temperature, the charging and discharging strategy of the energy storage system is adjusted, including: in response to the scheduling priority being a high priority, determining the power and energy required by the first device in the first device operating state; according to the power and energy required by the first device, the actual available power and energy of the energy storage battery, the maximum output capability and temperature of the power conversion system, the energy storage system power is distributed.

[0012] Further, the method includes: in the process of distributing the energy storage system power, the transient response of the energy storage battery and the temperature change rate of the power conversion system are monitored in real time; according to the real-time monitored transient response of the energy storage battery and the temperature change rate of the power conversion system, it is ensured that the decision of distributing the energy storage system power is within the safe operation boundary of the energy storage battery and the power conversion system.

[0013] The application further proposes that, in the process of distributing the energy storage system power, the transient response of the energy storage battery and the temperature change rate of the power conversion system are monitored in real time, including: collecting voltage data and current data of each module of the energy storage battery, and extracting transient response characteristics of each module; the transient response characteristics of each module are aggregated, consistency evaluation between modules is carried out, and differences between modules are identified; according to the result of the consistency evaluation between modules, the transient response characteristics of each module are weighted; according to the weighted transient response characteristics, the overall transient response of the energy storage battery is calculated; according to the overall transient response of the energy storage battery, the real-time health state evaluation of the energy storage battery is corrected, and based on the corrected real-time health state evaluation, the available power evaluation of the energy storage battery is corrected.

[0014] As an optional solution, the method further includes: synchronously collecting voltage signals, current signals and power conversion system temperature signals of the energy storage battery; performing frequency spectrum analysis on the voltage signals, current signals and temperature signals and filtering out electromagnetic interference; compensating the measurement deviation of the voltage signals, current signals and temperature signals based on sensor self-calibration parameters; according to the compensated data, transient voltage change rate, transient current change rate and temperature change rate are calculated respectively, and the transient voltage change rate, transient current change rate and temperature change rate are used as the judgment basis of the safe operation boundary.

[0015] In an embodiment, the operating state comprises device startup, normal operation, stop or switching condition; in response to the current operating state being device startup, a portion of the current available power of the energy storage battery is allocated as emergency reserve power, comprising: reading the typical power requirement and duration of the first device startup from the feature library; obtaining the available discharge power and available energy of the energy storage battery in real time; determining the reserved power as the minimum value of the typical power requirement and the available discharge power, and determining the reserved energy as the product of the reserved power and the duration.

[0016] In another embodiment, matching the first feature with the feature library determines the first device and the operating state of the first device, comprising: performing noise suppression on the power consumption load information, and then performing measurement bias compensation to obtain bias-compensated power consumption load data; performing multi-scale feature decomposition on the bias-compensated power consumption load data to obtain decomposed power consumption load feature components; comparing the decomposed power consumption load feature components with the feature library to determine the first device and the operating state of the first device.

[0017] In a second aspect, the application also discloses an industrial and commercial energy storage system energy management system, comprising: an establishment module for establishing a device power consumption feature library; the feature library comprises power consumption features of a specific device under different operating states; the specific device is a production device capable of generating instantaneous large power fluctuations; a feature extraction module for real-time monitoring of factory power consumption load information, performing feature extraction on the power consumption load information to obtain a first feature, and matching the first feature with the feature library to determine a first device and an operating state of the first device; an adjustment module for allocating a portion of the current available power of the energy storage battery as emergency reserve power according to the typical power requirement and duration of the first device recorded in the feature library; at the same time, adjusting the charge and discharge strategy of the energy storage system according to the operating state of the first device, the actual performance of the energy storage battery, and the service characteristics of the external power grid.

[0018] Advantages Compared with the prior art, the scheme of the application has remarkable excellent technical effects: first, by establishing a detailed equipment power consumption feature library and performing real-time matching, the application can accurately identify the source and characteristics of instantaneous high-power fluctuations in the factory, overcoming the problem that the prediction accuracy of existing energy management systems is greatly reduced when dealing with high-frequency, high-power and strong randomness power consumption shocks, so that the charge and discharge plan of the energy storage system is highly consistent with the actual power demand of the factory. Secondly, by allocating emergency reserve power, the application can effectively respond to sudden high-power demand, avoiding the situation of not being able to discharge in time and pulling high-priced electricity from the power grid when the power consumption soars, thereby reducing the cost of high-priced electricity purchase. Thirdly, combined with the device running state, the actual performance of the energy storage battery and the external power grid service characteristics to adjust the charge and discharge strategy, so that the energy storage system can more flexibly respond to real-time electricity price fluctuations and participate in frequency modulation auxiliary services, maximizing the overall economic benefit of the energy storage system, solving the problem that the existing method cannot effectively capture and respond to external power grid dynamic changes.

[0019] In summary, the energy management method of the industrial and commercial energy storage system of the application can effectively solve the challenges of battery performance degradation, high uncertainty of internal power consumption load and complex and variable external power grid environment, realize adaptive adjustment of the charge and discharge strategy of the energy storage system, and significantly improve the overall economic benefit of the energy storage system on the basis of ensuring the continuity of factory production and optimizing power consumption cost. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 is a flowchart of the steps of the energy management method of the industrial and commercial energy storage system disclosed by the embodiments of the application; Figure 2 is a structural schematic diagram of the energy management system of the industrial and commercial energy storage system disclosed by the embodiments of the application. DETAILED DESCRIPTION

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments belong. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments. The use herein of terms such as "comprise", "comprises", "comprising", "containing", "contains", "contain" or any other variation thereof is intended to cover a non-exclusive inclusion. Terms such as "first", "second", "third", "fourth", "fifth" and the like in the description of the embodiments herein are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of terms such as "first" and "second" and the like can not necessarily indicate that the corresponding elements so designated are the first and second elements respectively.

[0023] The implementation details of the technical solutions of the embodiments are described below in detail: In modern industrial and commercial production, industrial and commercial energy storage systems are widely deployed to optimize electricity usage, reduce operating costs, and improve energy utilization efficiency. These systems are usually equipped with energy management methods that develop charging and discharging strategies for the energy storage system based on time-of-use electricity prices, factory historical electricity consumption data, and production plans, to achieve "peak shaving" to save electricity costs. However, in actual operation, existing energy management methods face multiple challenges. The performance of energy storage batteries will deteriorate over time and with environmental factors, causing deviations between the actual available power, the actual storable power, and the charging and discharging efficiency of the energy storage system and the expected performance when performing the "peak shaving" task. Secondly, the electricity load characteristics of the production lines within the factory are becoming increasingly complex, for example, the introduction of new production lines can cause transient high-power fluctuations in electricity load, and the fluctuation time points and duration have high randomness and uncertainty. The existing energy management system's electricity load prediction strategy is mainly based on relatively stable historical data, and when dealing with such high-frequency, high-power, and highly random electricity surges, the prediction accuracy is greatly reduced. Furthermore, the external power grid environment has also become more dynamic and complex, for example, the introduction of real-time electricity pricing mechanisms and frequency regulation auxiliary service markets brings potential additional cost risks and revenue opportunities for industrial and commercial energy storage systems. Existing energy management methods mainly focus on fixed "peak shaving" strategies and cannot effectively capture and respond to real-time electricity price fluctuations, nor can they participate in frequency regulation auxiliary services that require fast and accurate power regulation. In the context of battery performance degradation, highly uncertain internal electricity load, and complex and dynamic external power grid environment, existing energy management methods cannot effectively adapt to the charging and discharging strategies of the energy storage system, thus failing to maximize the overall economic benefits of the energy storage system while ensuring factory production continuity and electricity cost optimization.

[0024] To this end, the present application proposes an industrial and commercial energy storage system energy management method, as shown in Figure 1 The method comprises: S101, establishing a device electricity characteristic library; the characteristic library comprises electricity characteristics of a specific device in different operating states; the specific device is a production device capable of generating transient high-power fluctuations; S102, real-time monitoring of factory power load information, extracting features from the power load information to obtain first features, matching the first features with the feature library to determine the first device and the first device running state; S103, according to the typical power demand and duration of the first device recorded in the feature library, a part of the current available power of the energy storage battery is allocated as emergency reserve power; at the same time, according to the first device running state, the actual performance of the energy storage battery, and the external power grid service characteristics, the charging and discharging strategy of the energy storage system is adjusted.

[0025] The industrial and commercial energy storage system energy management method proposed in the present application aims to solve the shortcomings of existing energy storage systems in dealing with battery performance degradation, complex load fluctuations and dynamic power grid environment. Among them, "device power consumption feature library" is a core concept, which stores the power consumption characteristics of specific production equipment under different running states, especially those that can produce instantaneous large power fluctuations. These power consumption characteristics can be instantaneous power curve, power change rate, current waveform characteristics or state timing time, etc., which are stored in the form of structured data, such as containing specific device ID, running state and corresponding power consumption characteristics. By establishing such a feature library, the system can finely identify and predict the complex load inside the factory.

[0026] The embodiments of the present application provide an industrial and commercial energy storage system energy management method, which establishes a device power consumption feature library, real-time monitors and analyzes the factory power load, and dynamically adjusts the charging and discharging strategy of the energy storage system to cope with instantaneous large power fluctuations and optimize energy management.

[0027] In a specific implementation, first, a device power consumption feature library needs to be established. The establishment of the feature library can be achieved in various ways. For example, all production devices in the factory that can generate instantaneous high-power fluctuations can be pre-tested and data collected in detail. During the testing process, the device is allowed to run in different operating states (such as starting, normal operation, stopping, or switching conditions), and its instantaneous power curve, power change rate, current waveform characteristics, and state timing time are recorded. After processing, these data are stored in the feature library in a structured form, and each entry contains a specific device ID, corresponding operating state, and detailed power consumption characteristics. Another way is to analyze and identify patterns in historical power consumption data using machine learning algorithms at the beginning of system operation, automatically extract and summarize the power consumption characteristics of different devices, and store them in the feature library. For example, a large punch press will generate a huge power spike at the start, which may only last a few hundred milliseconds to a few seconds. By collecting power data of the punch press at the start multiple times, the typical start power curve and duration can be obtained and stored as power consumption characteristics in the "start" operating state.

[0028] Secondly, the system needs to monitor the power load information of the factory in real time. This is usually achieved by installing high-precision power metering devices on the factory's main incoming line or key production lines. The monitored power load information is continuously collected and transmitted to the energy management system. Then, the system extracts the features from the power load information to obtain the first features. The feature extraction methods can include but are not limited to Fourier transform of the original load data to analyze frequency components, or wavelet analysis to capture transient changes, or direct calculation of instantaneous power, power change rate, etc. For example, when a rapidly rising power waveform is detected in the factory's total load, the system extracts the rising edge slope, peak power, and duration of the waveform as the first features.

[0029] Next, the system matches the obtained first features with the pre-established device power consumption feature library to determine which first device is currently running and the operating state of the first device. The matching process can use various algorithms, such as similarity matching based on pattern recognition, neural network classifier, or support vector machine, etc. For example, if the extracted first features are highly consistent with the power consumption characteristics of the "punch press-start" state in the feature library, the system can determine that the punch press is currently starting.

[0030] Finally, based on the matching results, i.e., the determined first device and its operating state, the system will adjust the charging and discharging strategy of the energy storage system. Specifically, the system will allocate a portion of the current available power of the energy storage battery as an emergency reserve power according to the typical power demand and duration of the first device recorded in the feature library. For example, if it is determined that the punch press is starting, and its typical power demand is 500 kW with a duration of 2 seconds, the system will immediately reserve 500 kW from the current available discharge power of the energy storage battery as an emergency reserve to cope with the upcoming power spike. At the same time, the system will also consider the operating state of the first device, the actual performance of the energy storage battery (such as the current state of charge, health state, internal resistance, etc.), and the external grid service characteristics (such as real-time electricity price, grid frequency regulation response requirements, etc.) to adjust the overall charging and discharging strategy of the energy storage system. For example, during the start-up of the punch press, the energy storage system may prefer to discharge to meet the internal load demand, avoiding pulling high-priced electricity from the grid; while during off-peak hours, if the electricity price is low and the energy storage battery has sufficient capacity, the system may choose to charge for subsequent use or participate in grid auxiliary services.

[0031] The industrial and commercial energy storage system energy management method of the present application realizes accurate identification and prediction of instantaneous high-power fluctuation production equipment within the factory by introducing a device power consumption feature library. Traditional methods often rely on macro load prediction, making it difficult to respond to sudden and random power surges, resulting in delayed response or strategy errors of the energy storage system. By establishing a detailed device power consumption feature library, the present application can monitor and identify the operating state of a specific device in real time, thereby predicting its power demand in advance. For example, when the system identifies that a large-scale device is about to start, it can immediately allocate emergency reserve power from the energy storage battery to ensure that the energy storage system can provide sufficient power in a timely manner when the power spike arrives, effectively reducing the load peak and avoiding high-priced electricity from the grid.

[0032] In addition, the method of the present application also considers the actual performance of the energy storage battery and the external grid service characteristics. Existing technologies often base their strategies on the initial performance of the battery or simplified models, ignoring the impact of battery performance degradation on actual charging and discharging capacity. By evaluating the actual performance of the energy storage battery in real time, the present application can more accurately determine the available power and energy of the battery, thereby developing a charging and discharging strategy that is more in line with actual conditions and avoiding strategy failures due to insufficient battery performance. At the same time, by considering the real-time price and response requirements of the external grid, the method of the present application can maximize the revenue from participating in external auxiliary service markets while meeting internal load demand, or charge during low-price periods to maximize economic benefits.

[0033] To more accurately describe the power consumption characteristics of specific equipment in different operating states, the present application further specifies the specific content of the power consumption characteristics and its storage method. The above-mentioned power consumption characteristics include instantaneous power curve, power change rate, current waveform characteristics, and state timing time. The method comprises: storing the power consumption characteristics in a structured data form in the feature library. The structured data form includes specific equipment ID, operating state, and the power consumption characteristics.

[0034] Specifically, the instantaneous power curve refers to the trajectory of the power change of the equipment over time within a short period of time, which can reflect the dynamic response of the equipment during startup, stop, or working condition switching. The power change rate quantifies the speed of power change, which is crucial for identifying instantaneous large power fluctuations. The current waveform characteristics can reveal the electrical characteristics of the equipment, such as the presence of harmonics or transient impacts. The state timing time records the duration of the equipment in a specific operating state, which helps to determine the stability of the equipment operation.

[0035] Among them, the power consumption characteristics are stored in a structured data form in the feature library, aiming to improve the manageability and retrieval efficiency of the data. The structured data form can be understood as a data structure containing multiple fields, such as a table structure in a database or a JSON format data object. In this structured data form, the specific equipment ID is used to uniquely identify each production equipment in the factory; the operating state is used to distinguish different working modes of the equipment, such as startup, normal operation, stop, or switching working conditions; and the power consumption characteristics include the above-mentioned instantaneous power curve, power change rate, current waveform characteristics, and state timing time. The purpose is to ensure that the data in the feature library can be efficiently stored, retrieved, and utilized, providing accurate data basis for subsequent load information matching and equipment state determination.

[0036] Through the above technical solution, since the power consumption characteristics are specifically refined into instantaneous power curve, power change rate, current waveform characteristics, and state timing time, and are stored in a structured data form, the information in the feature library is more rich and accurate. Therefore, when matching the power consumption load information with the feature library, more accurate equipment identification and operating state determination can be achieved. This accurate identification capability helps to more accurately predict the instantaneous large power fluctuation demand of specific equipment, thereby optimizing the allocation of emergency reserve power, and providing a more solid data support for the charge and discharge strategy adjustment of the energy storage system, thereby improving the intelligence and response speed of the energy management of the entire industrial and commercial energy storage system.

[0037] The application further proposes that the above-mentioned energy storage system comprises a power conversion system; the above-mentioned external power grid service characteristics comprise real-time price and response requirement; and the charging and discharging strategy of the energy storage system is adjusted according to the first device operating state, the actual performance of the energy storage battery, and the external power grid service characteristics, comprising: The predicted power demand and duration of the internal load peak corresponding to the first device operating state are evaluated, and the current external power grid service characteristics are queried; The electricity cost that can be saved by covering the internal load peak is calculated according to the predicted power demand and duration of the internal load peak; and the expected income brought by participating in external auxiliary services is calculated according to the real-time price and response requirement; The benefit trade-off result is obtained by comparing the saved electricity cost with the expected income; and the scheduling priority of the energy storage system power is determined according to the influence degree of the first device operating state on production; The actual available power and energy of the energy storage battery, and the maximum output capacity and temperature corresponding to the power conversion system are obtained; The charging and discharging strategy of the energy storage system is adjusted according to the scheduling priority, the benefit trade-off result, the actual available power and energy, and the maximum output capacity and temperature.

[0038] Specifically, the energy storage system can be understood as a whole containing core components such as energy storage batteries and power conversion systems. Among them, the power conversion system is the key equipment to realize the mutual conversion between the direct current of the energy storage battery and the alternating current of the power grid, and its maximum output capacity and operating temperature are important parameters affecting the overall performance and safety of the energy storage system. The external power grid service characteristics, such as real-time price and response requirement, refer to the price signal set by the power grid operator according to the power supply and demand relationship, market mechanism or specific demand (such as peak load shifting, frequency regulation, etc.), and the requirements for response speed, duration, etc. of the energy storage system. Real-time price can include time-of-use price, real-time market price, etc., while response requirement may involve power response speed, continuous discharge time, etc.

[0039] When adjusting the charging and discharging strategy of the energy storage system, it is necessary to first evaluate the predicted power demand and duration of the internal load peak corresponding to the first device operating state. For example, when the first device starts, a short but high amplitude power demand may be generated, and its size and duration need to be accurately predicted. At the same time, the current external power grid service characteristics are queried to understand whether there is an opportunity to participate in external auxiliary services and its potential income.

[0040] Subsequently, according to the expected power requirement and duration of the internal load spike, the cost of electricity saved by covering the spike through the energy storage system can be calculated. For example, during the peak period of electricity price, by discharging the energy storage to reduce the internal load spike, the high peak electricity charge can be avoided. At the same time, according to the real-time price and response requirements, the expected income brought by participating in external auxiliary services (such as demand response, frequency modulation service, etc.) is calculated.

[0041] On this basis, the saved cost of electricity and the expected income are compared to obtain the benefit trade-off result. The result will guide the system to make the optimal choice between internal load management and external market participation. In addition, according to the influence degree of the first device operating state on production, the scheduling priority of the energy storage system power is determined. For example, if the first device is a critical production device, its stable operation is crucial to production, and the priority of the load spike suppression should be higher than participating in external auxiliary services.

[0042] Finally, the actual available power and energy of the energy storage battery, and the maximum output capacity and temperature corresponding to the power conversion system are obtained. These parameters reflect the real-time operating state and physical limitations of the energy storage system. Considering the above scheduling priority, benefit trade-off result, actual available power and energy, and maximum output capacity and temperature, the charging and discharging strategy of the energy storage system is finally adjusted to maximize economic benefits and ensure safe and stable operation of the system.

[0043] The scheme of the present application introduces the power conversion system as a key component of the energy storage system, and considers its operating limitations in detail, and at the same time, the characteristics of external power grid services are taken into account in the adjustment of the charging and discharging strategy, thereby solving the limitations that may exist in the strategy adjustment of the basic scheme. Specifically, through accurate evaluation of internal load spikes and quantitative calculation of external power grid service benefits, the system can make fine economic benefit trade-off. In addition, by introducing the scheduling priority, it ensures that while pursuing economic benefits, the stable operation of critical production devices can be prioritized. More importantly, by obtaining the actual operating parameters of the energy storage battery and the power conversion system in real time, such as available power, energy, maximum output capacity and temperature, the adjustment of the charging and discharging strategy can fully consider the physical boundaries of the system, avoiding overload or improper operation, thereby improving the safety and reliability of the system.

[0044] In some preferred embodiments, assume a factory has a large punch press as the first device, which will generate a transient power spike of 500 kW lasting about 10 seconds when starting. On a certain day, the factory's energy management system monitors that the punch press is about to start and identifies its operating state. At the same time, the system queries that the current power grid is in the peak time electricity price stage, the electricity price is 1.5 yuan / kWh, and the power grid is carrying out a demand response activity, providing a subsidy of 0.2 yuan / kWh, requiring a response power of no less than 100 kW, and a duration of at least 30 minutes.

[0045] At this time, the system will evaluate the 500 kW load spike generated by the punch press starting and calculate the electricity cost that can be saved if the spike is covered by the energy storage system. For example, if the spike lasts for 10 seconds, the electricity cost saved is 500 kW * (10 / 3600)h * 1.5 yuan / kWh = 2.08 yuan. At the same time, the system calculates the expected income that may be brought by participating in the demand response activity. Assuming that the energy storage system can provide a response power of 200 kW for 30 minutes, the expected income is 200 kW * (30 / 60)h * 0.2 yuan / kWh = 20 yuan.

[0046] After comparing the electricity cost saved (2.08 yuan) with the expected income (20 yuan), the system obtains that the benefit of participating in external auxiliary services is higher. However, considering that the punch press is a key production device of the factory, its stable operation has a high priority in terms of the impact on production. At this time, the system will obtain the actual available power and energy of the energy storage battery (for example, the available discharge power is 600 kW, and the available energy is 1000 kWh), as well as the maximum output capability (for example, 700 kW) and the current temperature (for example, 45°C) of the power conversion system.

[0047] Based on these information, the system will adjust the charging and discharging strategy. Although the economic benefit of participating in external auxiliary services is higher, due to the high priority of the punch press, the system will prioritize ensuring that the load spike during the punch press starting is suppressed to guarantee the continuity of production. Therefore, the energy storage system will be scheduled to provide 500 kW of power support during the punch press starting to reduce the internal load spike. After the punch press starting is completed, if the energy storage system still has sufficient available power and energy, and the temperature of the power conversion system is within the safe range, the system can adjust the strategy to participate in the demand response activity at a lower power (for example, 100 kW) for the remaining time, so as to obtain part of the external income without affecting the production. In this way, the scheme of the present application can realize the dynamic balance between internal load management and external market participation, and fully consider the operation limitations of the system itself.

[0048] In some embodiments of the present application, the adjustment of the charging and discharging strategy of the energy storage system is based on the scheduling priority, the benefit trade-off result, the actual available power and energy, and the maximum output capacity and temperature. However, in actual industrial and commercial production environments, when high-priority production load demands are encountered, such as transient large power fluctuations in the startup or operation of critical production equipment, if only the comprehensive trade-off result is used for strategy adjustment, it may not be able to respond to these sudden high-priority demands in a timely and accurate manner, thereby affecting the continuity and stability of production, and even causing production interruption or efficiency reduction. In this regard, the present application further proposes a more refined strategy adjustment method to ensure that the power demand of core production equipment can be prioritized at critical moments.

[0049] According to the above scheduling priority, the above benefit trade-off result, the above actual available power and energy, and the above maximum output capacity and temperature, the charging and discharging strategy of the energy storage system is adjusted, including: in response to the above scheduling priority being a high priority, determining the power and energy required by the above first device in the above first device running state; and distributing the energy storage system power according to the above power and energy required by the above first device, the above actual available power and energy of the above energy storage battery, and the above maximum output capacity and temperature of the above power conversion system.

[0050] Specifically, when the scheduling priority is determined to be a high priority, it generally means that there is a power demand that is critical to production continuity or equipment safety, such as a transient large power impact when a specific device starts, or a continuous and stable power supply demand for a critical production link. At this time, the system will prioritize responding to such high-priority events. Determining the power and energy required by the first device in the first device running state means that the system accurately calculates the instantaneous power and total energy required to meet the normal operation of the device according to the real-time monitored first device running state, combined with the typical power demand and duration of the device in the current running state recorded in the device power consumption feature library. For example, if the first device is in a startup state, the peak power and total startup energy will be determined according to its startup curve. The actual available power and energy of the energy storage battery refers to the maximum discharge power and total energy that the energy storage battery can provide under the condition of meeting its own safe operation (such as state of charge, temperature, cycle life, etc.) at the current time. The maximum output capacity and temperature of the power conversion system refers to the maximum power that the power conversion system can safely and stably convert from the direct current of the energy storage battery to alternating current and output under the current operating environment, as well as its current working temperature, in order to avoid overload or overheating. Distributing the energy storage system power means that after considering the above parameters, the system intelligently decides how much power to output from the energy storage battery, and delivers it to the factory power grid through the power conversion system, in order to meet the high-priority power demand of the first device. This distribution process aims to maximize the protection of high-priority loads, while also considering the safe operation and long-term health of the energy storage system.

[0051] The scheme of the present application solves the above problems by preferentially and accurately determining the power and energy required by the first device when the dispatching priority is high priority, and combining the actual available power and energy of the energy storage battery and the maximum output capacity and temperature of the power conversion system to perform targeted power allocation. Specifically, when the system identifies a high priority event, it no longer relies solely on the comprehensive benefit trade-off result, but instead prioritizes ensuring the operation of critical production equipment. By accurately calculating the instantaneous demand of the first device and matching it with the real-time capacity of the energy storage system, the required emergency power can be provided in the shortest time, effectively avoiding production interruptions caused by power shortages. This mechanism enables the energy storage system to transition from a passive auxiliary role to an active safeguard role, especially when dealing with transient large power fluctuations, its response speed and power supply stability are significantly improved.

[0052] Through the above technical scheme, the present application can significantly improve the response speed and power supply reliability of industrial and commercial energy storage systems when dealing with high priority production loads. In particular, when transient large power fluctuations occur during the startup or operation of critical production equipment, the system can quickly identify and prioritize these emergency demands, effectively avoiding the impact of power grid fluctuations or power shortages on production, ensuring the continuity and stability of production. In addition, through fine-grained power allocation, the energy storage system can more efficiently utilize its available resources, ensuring core loads while maximizing the service life of energy storage batteries and power conversion systems, improving the economic efficiency and operational safety of the entire system.

[0053] In some preferred embodiments, assume that a large punch machine (as the first device) in a factory is about to start, which will produce a transient power demand of up to 500kW at the moment of startup and last for about 10 seconds. The system identifies through real-time monitoring that the punch machine is about to start, and determines its operating state as "device startup" according to the typical power demand of the device in the startup state recorded in the device power consumption feature library, and the dispatching priority is determined as high priority. At this time, the system will immediately query the current available discharge power of the energy storage battery as 600kW, the available energy as 100kWh, and the maximum output capacity of the power conversion system as 550kW, and the current temperature is within the normal range. According to the 500kW power required by the punch machine and the 10 second duration, the system will preferentially allocate 500kW of power from the energy storage battery and output through the power conversion system to ensure the smooth startup of the punch machine. Even if the external grid price is low at this time, the system will not prioritize participating in grid auxiliary services, but will prioritize resources to ensure the startup of the punch machine, thereby avoiding production delays or equipment damage caused by power shortages.

[0054] The application further provides an energy management method for a commercial and industrial energy storage system, which comprises: In the process of distributing the energy storage system power, the transient response of the energy storage battery and the temperature change rate of the power conversion system are monitored in real time; According to the real-time monitored transient response of the energy storage battery and the temperature change rate of the power conversion system, it is ensured that the decision of distributing the energy storage system power is within the safe operation boundary of the energy storage battery and the power conversion system.

[0055] Specifically, the transient response of the energy storage battery refers to the rapid dynamic response characteristics of the internal voltage, current, temperature and other parameters of the energy storage battery when the power or current thereof changes dramatically in a short time. Such response can reflect the internal state and health condition of the battery under the condition of transient high load or rapid charge and discharge. The temperature change rate of the power conversion system refers to the speed of the temperature change of the key components (such as IGBT module, transformer, etc.) of the power conversion system over time. Too fast temperature rising rate may indicate that the system is overloaded or poorly cooled, and there is potential risk. The safe operation boundary can be understood as the maximum or minimum threshold range of various parameters (such as voltage, current, temperature, power change rate, etc.) allowed by the energy storage battery and the power conversion system during design and operation. Exceeding these boundaries may lead to performance degradation, shortened life, and even failure of the equipment.

[0056] The scheme of the application can dynamically obtain the actual operation state information of the core components of the energy storage system by introducing real-time monitoring of the transient response of the energy storage battery and the temperature change rate of the power conversion system. When the system is distributing the energy storage system power, these real-time monitoring data are used to evaluate the influence of the current distribution decision on the safety of the equipment. For example, if it is monitored that the transient response of the energy storage battery is too dramatic, or the temperature change rate of the power conversion system is too fast, it indicates that the current power distribution may make the equipment approach or exceed its safe operation boundary. Therefore, the system can timely adjust or limit the amplitude and rate of power distribution to avoid irreversible damage to the equipment. This mechanism ensures that while meeting the load demand and economic benefits, the operation of the energy storage system is always within a controlled safe range.

[0057] In some preferred embodiments, assume that at a certain time, according to the scheduling priority and the benefit trade-off result, the system decides to allocate a large instantaneous power from the energy storage battery to respond to the starting demand of the first device in the factory. Before or at the same time as executing this power allocation decision, the system will monitor the voltage and current rate of change of the energy storage battery in real time to evaluate its transient response, and monitor the temperature sensor data of the key points of the power conversion system to calculate its temperature change rate. If the monitoring data shows that this large power allocation will cause the transient voltage drop of the energy storage battery to exceed the preset safety threshold, or the temperature rise rate of a certain component of the power conversion system is too fast, the system will immediately trigger a warning and modify the original power allocation plan. For example, the system may appropriately reduce the allocated instantaneous power peak or slow down the slope of the power output to ensure that the energy storage battery and the power conversion system work within the safe operating boundary. This dynamic adjustment mechanism avoids potential damage to the device caused by blindly pursuing power output, thereby ensuring the long-term healthy operation of the energy storage system.

[0058] In some embodiments of the present application described above, it is proposed that in the process of allocating power of the energy storage system, the transient response of the energy storage battery and the temperature change rate of the power conversion system need to be monitored in real time to ensure that the power allocation decision is within the safe operating boundary. Specifically, the real-time monitoring of the transient response of the energy storage battery and the temperature change rate of the power conversion system in the process of allocating power of the energy storage system can include the following steps: Collecting voltage data and current data of each module of the energy storage battery and extracting transient response characteristics of each module; Aggregating the transient response characteristics of the modules to evaluate the consistency between the modules and identify the differences between the modules; according to the results of the consistency evaluation between the modules, the transient response characteristics of the modules are weighted processed; According to the weighted processed transient response characteristics, the overall transient response of the energy storage battery is calculated; According to the overall transient response of the energy storage battery, the real-time health status evaluation of the energy storage battery is corrected, and based on the corrected real-time health status evaluation, the available power evaluation of the energy storage battery is corrected.

[0059] Specifically, collecting voltage data and current data of each module of the energy storage battery means that through the sensors deployed inside the energy storage battery system, the voltage and current values of each battery module are obtained in real time. These data are the basis for evaluating the transient behavior of the battery module. Based on the collected voltage data and current data, the transient response characteristics of each module can be extracted, such as the instantaneous change rate of voltage or current, response time, overshoot, etc. These characteristics can reflect the response ability of the battery module to power demand changes in a short time.

[0060] Among them, the transient response characteristics of each module are aggregated for inter-module consistency evaluation, aiming to identify the performance differences between different battery modules. Due to the influence of factors such as battery manufacturing process, use environment, aging degree, etc., the actual performance of each module may deviate. By comparing and analyzing these transient response characteristics, the inconsistency between modules can be quantified. For example, the mean, variance or standard deviation of the transient response characteristics of each module can be calculated to evaluate its dispersion. According to the results of inter-module consistency evaluation, the transient response characteristics of each module can be weighted. For example, for modules with poor performance or low consistency, a lower weight can be given to reduce their impact on overall evaluation; on the contrary, for modules with excellent performance or high consistency, a higher weight can be given. This weighting process helps to more accurately reflect the overall performance of the energy storage battery system.

[0061] In practical applications, according to the weighted transient response characteristics, the overall transient response of the energy storage battery can be calculated. This can be achieved by aggregating (such as weighted average) the weighted transient response characteristics of each module, thereby obtaining a comprehensive index that can represent the transient behavior of the entire energy storage battery system.

[0062] Further, according to the overall transient response of the energy storage battery, the real-time health status evaluation of the energy storage battery can be corrected. Transient response characteristics are closely related to health status indicators such as internal impedance and capacity decay of the battery. For example, a slower transient response or an increased overshoot may indicate an increase in internal impedance or a decrease in capacity. By incorporating the overall transient response into the health status evaluation model, the accuracy and real-time performance of the evaluation can be improved. Based on the corrected real-time health status evaluation, the available power evaluation of the energy storage battery can be further corrected. The available power of the battery not only depends on its current state of charge, but also is affected by its health status and transient response capability. A battery with good health status and rapid transient response can usually provide more power in a short period of time. Therefore, by correcting the health status evaluation, the actual available power of the energy storage battery under different working conditions can be more accurately predicted, providing a more reliable basis for power allocation decisions.

[0063] The scheme of the present application can overcome the errors caused by the traditional method of relying only on the overall battery parameters or simple average processing by collecting and extracting the voltage data and current data of each module of the energy storage battery in detail, and further performing consistency evaluation and weighted processing between modules. It is precisely because the differences of each module are identified and quantified, and the transient response characteristics are weighted accordingly, that the calculated overall transient response of the energy storage battery can more truly reflect the actual dynamic performance of the system. On this basis, by integrating the overall transient response into the real-time health state evaluation and further correcting the available power evaluation, it is ensured that the energy storage system can fully consider the actual operating conditions and dynamic response capability of the battery when distributing power, avoiding overload or performance waste caused by inaccurate information.

[0064] The present application further proposes that the above method further comprises: synchronously collecting voltage signals, current signals and power conversion system temperature signals of the energy storage battery; performing frequency spectrum analysis on the voltage signals, current signals and temperature signals and filtering out electromagnetic interference; compensating the measurement deviation of the voltage signals, current signals and temperature signals based on sensor self-calibration parameters; calculating the transient voltage change rate, the transient current change rate and the temperature change rate according to the compensated data, and taking the transient voltage change rate, the transient current change rate and the temperature change rate as the judgment basis of the safe operation boundary.

[0065] Specifically, synchronously collecting voltage signals, current signals and power conversion system temperature signals of the energy storage battery means that these key operating parameters are obtained by high-precision sensors at the same time point or extremely short time interval to ensure the time consistency of the data and provide accurate original data for subsequent analysis. Among them, the voltage signals and current signals reflect the charge and discharge state and transient response characteristics of the energy storage battery, and the power conversion system temperature signal is directly related to its thermal load and operating stability.

[0066] Further, performing frequency spectrum analysis on the voltage signals, current signals and temperature signals and filtering out electromagnetic interference aims to improve the purity and reliability of the data. In actual business environment, electromagnetic interference exists universally and may cause distortion of the measured signals. Through frequency spectrum analysis, the interference frequency can be identified and located, and then digital filter and other technologies are used to effectively filter out these interferences to ensure that the subsequent calculation is based on real and effective signals.

[0067] In addition, compensating the measurement deviation of the voltage signals, current signals and temperature signals based on sensor self-calibration parameters aims to further improve the accuracy of the data. The sensor may produce drift or inherent deviation under long-term operation or environmental change. By pre-setting the self-calibration parameters or real-time calibration mechanism to compensate the original data collected, these systematic errors can be eliminated, and the data is closer to the true value.

[0068] Thereby, the transient voltage rate of change, the transient current rate of change and the temperature rate of change are calculated according to the compensated data, respectively, and the purpose is to obtain key indicators reflecting the dynamic characteristics of the system. The transient voltage rate of change and the transient current rate of change can sensitively capture the internal state changes of the energy storage battery in the process of rapid charging and discharging, such as overcharge / overdischarge risk, internal resistance mutation, etc.; the temperature rate of change can reflect the thermal management efficiency and potential overheating risk of the power conversion system. These rates of change are important parameters for evaluating the instantaneous stress level of the system.

[0069] Finally, the transient voltage rate of change, the transient current rate of change and the temperature rate of change are used as the basis for judging the safety operation boundary. This means that the safe operation of the energy storage battery and the power conversion system is no longer dependent on static voltage, current or temperature thresholds, but combines the dynamic change trend of these parameters. For example, when the transient voltage rate of change or the transient current rate of change exceeds the preset dynamic threshold, even if the voltage or current itself is still within the static safety range, it may indicate a potential risk, thereby triggering a warning or adjusting the power distribution strategy.

[0070] The scheme of the present application effectively solves the limitations that may exist in the traditional monitoring method by introducing a refined signal processing and dynamic parameter evaluation mechanism. First, the synchronous acquisition of multi-dimensional signals ensures the time consistency and comprehensiveness of the data, laying the foundation for comprehensive evaluation of the system state. Second, the spectrum analysis and electromagnetic interference filtering steps significantly improve the purity of the original data, avoiding the interference of noise on the judgment. Third, the measurement bias compensation based on the sensor self-calibration parameters further corrects the accuracy of the data, ensuring the reliability of subsequent calculations. It is precisely due to these preprocessing steps that the transient voltage rate of change, the transient current rate of change and the temperature rate of change calculated can truly and accurately reflect the real-time dynamic stress of the energy storage battery and the power conversion system. Finally, using these dynamic rates of change as the basis for judging the safety operation boundary enables the system to identify potential operating risks earlier and more accurately, thereby achieving more proactive and more refined safety management in power distribution decisions.

[0071] By the technical solution, the safety and reliability of the industrial and commercial energy storage system in the power distribution process can be improved. Through high-precision acquisition, denoising and deviation compensation of the key operation signals, the authenticity and accuracy of the monitoring data are ensured. Based on the transient change rate as the basis for judging the safe operation boundary, the system can switch from static threshold judgment to dynamic trend prediction, so as to more sensitively capture the potential risks of the energy storage battery and the power conversion system, realize earlier warning and intervention. This not only helps to avoid equipment overload or damage caused by instantaneous large power fluctuation, prolongs the service life of the energy storage battery and the power conversion system, but also effectively reduces the operation risk, improves the operation efficiency and safety of the entire energy storage system, and provides more stable and reliable energy management services for industrial and commercial users.

[0072] In some preferred embodiments, the following is described by a specific example. Assuming that a large punch in a factory is started, causing a transient large power peak of the factory power load. At this time, the energy storage system needs to respond quickly and distribute power to suppress the load. In the process of distributing the power of the energy storage system, the voltage signal, current signal and temperature signal of the power conversion system of the energy storage battery are synchronously collected. These original signals are first sent to the signal processing unit for frequency spectrum analysis to identify and filter out electromagnetic interference generated by other high-frequency devices in the factory. Then, the filtered signals are compensated for measurement deviation using pre-stored sensor self-calibration parameters to eliminate the error of the sensor itself. Based on the compensated high-precision data, the system calculates the transient voltage change rate, transient current change rate of the energy storage battery, and the temperature change rate of the power conversion system in real time. For example, if the transient current change rate exceeds the preset dynamic safety threshold (for example, more than 100 amperes per millisecond) in a very short time, even if the current value is still within the static safety range, the system will immediately judge that there is an overload risk, and adjust the power distribution strategy accordingly, such as reducing the discharge power of the energy storage system or switching to a backup strategy, thereby effectively avoiding damage to the energy storage battery due to instantaneous large current impact, while ensuring that the power conversion system will not overheat due to rapid temperature rise, thereby ensuring the safe and stable operation of the entire energy storage system when dealing with sudden load.

[0073] The application further proposes the step of allocating a part of the current available power of the energy storage battery as emergency reserve power, specifically including: the operating state includes device startup, normal operation, stop or switching working condition; In response to the current running state being device startup, a part of the current available power of the energy storage battery is allocated as emergency reserve power, including: the feature library reads the typical power demand and duration of the first device startup; real-time acquisition of the available discharge power and available energy of the energy storage battery; determination of the reserved power as the minimum value of the typical power demand and the available discharge power, and determination of the reserved energy as the product of the reserved power and the duration.

[0074] Specifically, the running state can be understood as the working state of a specific device at different time points or in different operation modes. For example, device startup refers to the moment when the device enters the working state from the static state, usually accompanied by a large impact current and power; normal operation refers to the continuous working state of the device under stable load; stop refers to the device closing from the working state; switching working condition refers to the conversion between different production modes or load levels. These states all have unique power consumption characteristics. Among them, in response to the current running state being device startup, it means that when the system identifies that the first device is about to or is entering the startup state, a specific emergency reserve power allocation mechanism will be triggered. This mechanism first reads the typical power demand and duration required by the first device during startup from the device power consumption feature library. For example, for a large motor, its startup power may be much higher than the rated operating power, and the duration is short. At the same time, the system will monitor and acquire the current available discharge power and available energy of the energy storage battery in real time to understand the actual power supply capacity of the energy storage battery. On this basis, the determination method of the reserved power is to take the minimum value of the typical power demand and the available discharge power of the energy storage battery. This minimum value taking method aims to ensure that the allocated emergency reserve power can meet the minimum demand of device startup, and will not exceed the current actual discharge capacity of the energy storage battery, thereby avoiding problems caused by battery overload or insufficient reservation. The reserved energy is obtained by multiplying the determined reserved power and the duration to ensure sufficient energy reserve during the entire startup process.

[0075] The scheme of the present application defines a plurality of operating states, and designs a set of refined emergency reserve power allocation mechanism for the high power demand scenario of device startup, effectively solving the potential deficiencies of the basic scheme in dealing with instantaneous large power fluctuation. Specifically, by obtaining the typical power demand and duration of the first device startup from the feature library, the system can pre-understand the energy and power peak required for device startup. At the same time, the available discharge power and available energy of the energy storage battery are obtained in real time, ensuring accurate understanding of the state of the energy storage system itself. Comparing the typical power demand with the available discharge power of the energy storage battery and taking the minimum value as the reserved power, this strategy cleverly balances the device demand and battery capacity, ensuring the reliability of device startup and avoiding resource waste caused by blind reservation. Thus, the energy storage system can more intelligently and accurately respond to the instantaneous large power demand during device startup, improving the robustness and economy of the entire system.

[0076] In some preferred embodiments, it is assumed that the startup process of a large punch press (as the first device) in a factory is identified as the current operating state. Through the device power consumption feature library query, it is known that the typical power demand of the punch press during startup is 500 kW, and the duration is 10 seconds. At the same time, it is monitored in real time that the current available discharge power of the energy storage battery is 400 kW, and the available energy is 200 kWh. According to the scheme of the present application, the reserved power will be determined as the minimum value between the typical power demand (500 kW) and the available discharge power (400 kW), i.e. 400 kW. This means that although the punch press theoretically needs 500 kW of instantaneous power, considering the current actual capacity of the energy storage battery, the system will allocate 400 kW as emergency reserve power. Further, the reserved energy will be determined as the product of the reserved power 400 kW and the duration 10 seconds (about 0.00278 hours), i.e. about 1.11 kWh. In this way, the energy storage system can provide accurate and feasible emergency power and energy support for the startup of the punch press, ensuring its stable startup, while avoiding invalid reservation beyond the actual capacity of the battery, thereby optimizing the operation strategy of the energy storage system.

[0077] In some embodiments of the present application, real-time monitoring of power load information in the factory is performed, and feature extraction is performed on the power load information to obtain first features, and then the first features are matched with a feature library to determine the first device and the running state of the first device. However, in actual application, the original power load information may be affected by environmental noise interference or sensor measurement deviation, resulting in inaccurate first features extracted, and thus affecting the reliability of the matching result. If the matching result is inaccurate, the production device with instantaneous high-power fluctuation and its running state cannot be accurately identified, thereby affecting the effectiveness of subsequent energy storage system charging and discharging strategy adjustment. In this regard, the present application further proposes a scheme of pre-processing and fine feature decomposition of power load information to improve the accuracy of device identification and state determination.

[0078] The above-mentioned matching of the first features with the feature library to determine the first device and the running state of the first device includes: noise suppression of the power load information, and then measurement deviation compensation to obtain deviation-compensated power load data; multi-scale feature decomposition of the deviation-compensated power load data to obtain decomposed power load feature components; comparison of the decomposed power load feature components with the feature library to determine the first device and the running state of the first device.

[0079] Specifically, noise suppression refers to removing random or periodic interference signals in the power load information that are not generated by device operation through digital signal processing techniques such as low-pass filtering, median filtering or wavelet denoising, etc., to improve the signal-to-noise ratio of the data. The purpose is to eliminate interference so that the subsequent feature extraction and matching process can be based on purer signals. Measurement deviation compensation refers to correcting systematic errors of the power load data after noise suppression. This can be achieved by pre-calibrating sensors, using adaptive calibration algorithms or statistically compensating based on historical data, etc. The purpose is to eliminate the inherent deviation of sensors or measurement systems to ensure the authenticity and reliability of the data. Thus, the deviation-compensated power load data is the power load data after noise suppression and measurement deviation compensation, which is closer to the true situation and provides high-quality input for subsequent feature decomposition.

[0080] Among them, the multi-scale feature decomposition is a technology for decomposing the power load data at different time scales or frequency scales. For example, wavelet transform, empirical mode decomposition (EMD) or Fourier transform can be used to decompose the original signal into multiple feature components with different frequency components. The purpose is to capture the power characteristics of the equipment at different granularities, such as instantaneous peak, steady fluctuation, periodic change, etc., so as to more comprehensively and meticulously represent the running state of the equipment. The power load feature component after decomposition refers to the sub-signal or coefficient obtained by multi-scale feature decomposition, which can reflect the characteristics of the power load at different scales. These components can more effectively capture the unique power mode of a particular device. In practical applications, comparison refers to comparing the power load feature components after decomposition with the power features of the specific device in different running states stored in the feature library. The comparison method can include but is not limited to correlation analysis, pattern recognition algorithm (such as support vector machine SVM, neural network NN), dynamic time warping (DTW), etc. The purpose is to accurately identify the first device currently running and its running state according to the similarity of the features.

[0081] The scheme of the present application first purifies the original power load information by introducing noise suppression and measurement bias compensation, effectively filtering out environmental interference and sensor errors, ensuring the accuracy and reliability of the input data. It is precisely because of the high-quality input data that the subsequent feature extraction and matching process can be based on more real and pure signals. On this basis, the multi-scale feature decomposition technology can deeply mine the internal mode of the power load from different time or frequency dimensions, capture subtle features that traditional single-scale analysis cannot discover, such as transient impact when the device starts, periodic fluctuations when it runs normally, and complex changes when the working condition switches. By comparing these fine and multi-scale feature components with the power features in the feature library that have also been fine processed, the accuracy and robustness of the matching can be significantly improved, so as to more accurately identify the production equipment with instantaneous high-power fluctuations and its current running state.

[0082] By the above technical solution, since the power load information is subjected to noise suppression and measurement deviation compensation, the purity and accuracy of the data are effectively improved, and misjudgment caused by data quality problems is avoided. Further, through multi-scale feature decomposition, the unique power consumption mode of a specific device under different operating states can be more comprehensively and deeply captured, so that the feature representation capability is significantly enhanced. Therefore, when the decomposed power load feature components are compared with the feature library, higher-precision device recognition and operating state judgment can be achieved, and the misrecognition rate and the missed recognition rate are significantly reduced. Such accurate recognition capability provides a reliable basis for the fine adjustment of the subsequent energy storage system charging and discharging strategy, ensures that the allocation of emergency reserve power and the adjustment of the charging and discharging strategy can more timely and accurately respond to actual production demands, and thus improves the efficiency and economic benefits of the entire industrial and commercial energy storage system energy management.

[0083] In some preferred embodiments, assuming that there is a large punch press in the factory, which will generate a short-duration but high-power transient peak load when starting. In order to accurately identify the starting state of the punch press and respond in time, first, the total power load information collected from the power grid side of the factory is subjected to noise suppression, for example, using a wavelet denoising algorithm to filter out high-frequency electromagnetic interference. Subsequently, by comparing with the preset sensor calibration parameters, the voltage and current measurement values are subjected to deviation compensation to eliminate measurement errors caused by sensor aging or environmental temperature changes, to obtain the deviation-compensated power load data. Then, the deviation-compensated data is subjected to multi-scale feature decomposition, for example, using empirical mode decomposition (EMD) to decompose the signal into multiple intrinsic mode functions (IMFs), each of which represents power consumption characteristics of different time scales. Among them, the high-frequency IMF may reflect the transient impact of the punch press starting, and the low-frequency IMF reflects the baseline load of its steady-state operation. Finally, these decomposed power load feature components, especially the IMFs related to the punch press starting characteristics, are compared with the multi-scale feature templates of the punch press starting pre-stored in the feature library. By calculating the similarity (for example, using Euclidean distance or correlation coefficient) between these feature components, it can be accurately determined that the current power load is caused by the punch press starting, and it is identified that it is in the starting operating state.

[0084] The specific embodiments of the present application also disclose an industrial and commercial energy storage system energy management system, as shown in Figure 2 The system comprises: The establishing module 201 is configured to establish a device power consumption feature library; the feature library comprises power consumption features of a specific device under different operating states; the specific device is a production device capable of generating transient high-power fluctuations; The feature extraction module 202 is configured to monitor the power load information of the factory in real time, extract features from the power load information to obtain first features, match the first features with the feature library to determine a first device and a first device running state. The adjustment module 203 is configured to allocate a part of the current available power of the energy storage battery as an emergency reserve power according to the typical power demand and duration of the first device recorded in the feature library; and adjust the charging and discharging strategy of the energy storage system according to the first device running state, the actual performance of the energy storage battery, and the external power grid service characteristics.

[0085] The above merely describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An energy management method for industrial and commercial energy storage systems, characterized in that, The method includes: Establish a power consumption characteristic database for equipment; the database includes the power consumption characteristics of specific equipment under different operating conditions; the specific equipment is production equipment capable of generating instantaneous high power fluctuations; Real-time monitoring of factory power load information; feature extraction of the power load information to obtain a first feature; matching the first feature with the feature library to determine the first device and the operating status of the first device. Based on the typical power demand and duration recorded by the first device in the feature library, a portion of the current available power of the energy storage battery is allocated as emergency reserve power; at the same time, the charging and discharging strategy of the energy storage system is adjusted according to the operating status of the first device, the actual performance of the energy storage battery, and the service characteristics of the external power grid.

2. The energy management method for an industrial and commercial energy storage system according to claim 1, characterized in that, The power consumption characteristics include instantaneous power curves, power change rates, current waveform characteristics, and state timing; the method includes: storing the power consumption characteristics in the characteristic library in a structured data form; the structured data form includes a specific device ID, operating status, and the power consumption characteristics.

3. The energy management method for an industrial and commercial energy storage system according to claim 1, characterized in that, The energy storage system includes a power conversion system; The external power grid service characteristics include real-time pricing and response requirements; Based on the operating status of the first device, the actual performance of the energy storage battery, and the service characteristics of the external power grid, the charging and discharging strategy of the energy storage system is adjusted, including: Assess the expected power demand and duration of the internal load peak corresponding to the operating state of the first device, and query the current external power grid service characteristics; Calculate the electricity cost savings that can be achieved by covering the internal load peaks based on the projected power demand and duration of the internal load peaks; calculate the expected benefits of participating in external ancillary services based on the real-time prices and response requirements; By comparing the saved electricity costs with the expected benefits, a cost-benefit trade-off is obtained; based on the impact of the first equipment's operating status on production, the scheduling priority of the energy storage system's power is determined. Obtain the actual usable power and energy of the energy storage battery, as well as the maximum output capacity and temperature of the power conversion system; The charging and discharging strategy of the energy storage system is adjusted based on the scheduling priority, the benefit trade-off result, the actual available power and energy, the maximum output capacity, and the temperature.

4. The energy management method for an industrial and commercial energy storage system according to claim 3, characterized in that, Adjusting the charging and discharging strategy of the energy storage system based on the scheduling priority, the benefit trade-off result, the actual available power and energy, the maximum output capacity and temperature, includes: in response to the scheduling priority being high priority, determining the power and energy required by the first device under the first device's operating state; and allocating the energy storage system power based on the power and energy required by the first device, the actual available power and energy of the energy storage battery, the maximum output capacity and temperature of the power conversion system.

5. The energy management method for an industrial and commercial energy storage system according to claim 4, characterized in that, The method includes: During the process of allocating power to the energy storage system, the transient response of the energy storage battery and the temperature change rate of the power conversion system are monitored in real time. Based on the real-time monitored transient response of the energy storage battery and the temperature change rate of the power conversion system, the decision to allocate power to the energy storage system is ensured to be within the safe operating boundaries of the energy storage battery and the power conversion system.

6. The energy management method for an industrial and commercial energy storage system according to claim 5, characterized in that, The process of allocating power to the energy storage system, including real-time monitoring of the transient response of the energy storage battery and the temperature change rate of the power conversion system, includes: Collect voltage and current data of each module of the energy storage battery, and extract the transient response characteristics of each module; The transient response characteristics of each module are aggregated to perform an inter-module consistency assessment and identify differences between modules; based on the results of the inter-module consistency assessment, the transient response characteristics of each module are weighted. The overall transient response of the energy storage battery is calculated based on the weighted transient response characteristics. Based on the overall transient response of the energy storage battery, the real-time health status assessment of the energy storage battery is corrected, and based on the corrected real-time health status assessment, the available power assessment of the energy storage battery is corrected.

7. The energy management method for an industrial and commercial energy storage system according to claim 5, characterized in that, The method further includes: synchronously acquiring the voltage signal, current signal, and temperature signal of the energy storage battery and the power conversion system; performing spectrum analysis on the voltage signal, current signal, and temperature signal and filtering out electromagnetic interference; The measurement deviations of voltage, current, and temperature signals are compensated based on the sensor's self-calibration parameters. The transient voltage change rate, transient current change rate, and temperature change rate are calculated based on the compensated data, and these transient voltage change rate, transient current change rate, and temperature change rate are used as the basis for judging the safe operating boundary.

8. The energy management method for an industrial and commercial energy storage system according to claim 1, characterized in that, The operating status includes equipment startup, normal operation, shutdown, or switching conditions; In response to the current operating state of device startup, the step of allocating a portion of the currently available power from the energy storage battery as emergency reserve power includes: Read the typical power requirements and duration of the first device during startup from the feature library; obtain the available discharge power and available energy of the energy storage battery in real time; The reserved power is determined to be the minimum of the typical power requirement and the available discharge power, and the reserved energy is determined to be the product of the reserved power and the duration.

9. The energy management method for an industrial and commercial energy storage system according to claim 1, characterized in that, The step of matching the first feature with the feature library to determine the first device and the operating status of the first device includes: The power load information is subjected to noise suppression, and then measurement deviation compensation is performed to obtain the power load data after deviation compensation. The electricity load data after deviation compensation is subjected to multi-scale feature decomposition to obtain the decomposed electricity load feature components. The decomposed electrical load characteristic components are compared with the feature library to determine the first device and its operating status.

10. An energy management system for industrial and commercial energy storage systems, characterized in that, The system includes: A module is established to create a database of equipment power consumption characteristics; the database includes the power consumption characteristics of specific equipment under different operating conditions; the specific equipment is production equipment capable of generating instantaneous high power fluctuations; The feature extraction module is used to monitor the factory's power load information in real time, extract features from the power load information to obtain a first feature, and match the first feature with the feature library to determine the first device and the operating status of the first device. The adjustment module is used to allocate a portion of the current available power of the energy storage battery as emergency reserve power based on the typical power demand and duration recorded by the first device in the feature library; at the same time, it adjusts the charging and discharging strategy of the energy storage system based on the operating status of the first device, the actual performance of the energy storage battery, and the service characteristics of the external power grid.

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