Energy storage charging and discharging management method and device under load fluctuation

Abstract

The invention discloses an energy storage charging and discharging management method and device under load fluctuation, and relates to the technical field of energy management, and the method comprises the steps: collecting the equipment operation and demand electric charge data of an enterprise; acquiring enterprise load fluctuation and electricity price floating data; according to the load fluctuation and the electricity price floating, peak and valley load time periods are determined; generating a charging and discharging strategy of the energy storage system based on the time period; equipment operation, electricity price and energy storage system state of an enterprise are monitored in real time, and optimization constraint conditions are generated; and inputting the optimization constraint condition into the demand electric charge calculation model, and further optimizing the charging and discharging strategy. The technical problem that an existing energy storage system cannot dynamically adapt to enterprise load fluctuation and electricity price change in the charging and discharging process is solved, and the technical effects of reducing the enterprise demand electric charge and improving the electric power utilization efficiency are achieved.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and in particular to a method and device for managing energy storage charging and discharging under load fluctuations. Background Art

[0002] In the scenario of enterprise electricity management, with the expansion of production scale and fluctuation of electricity demand, the problems faced by enterprises in the management of demand charges are becoming increasingly prominent. Traditional electricity management methods are often relatively simple and fixed, relying only on the basic electricity dispatching system, and it is difficult to flexibly adjust according to real-time electricity price fluctuations and load changes. Especially during peak load periods, enterprises cannot effectively avoid the increase in demand charges, and during trough periods, the charging efficiency of energy storage systems is not fully utilized. The traditional energy storage system charging and discharging strategy lacks accurate response to actual electricity demand, resulting in unreasonable allocation of power resources, and the charging and discharging power of energy storage equipment is not effectively optimized, which may even affect the operating efficiency of the system and the life of the equipment. Therefore, how to formulate a more flexible and optimized charging and discharging management strategy based on the actual load fluctuations, electricity price changes and energy storage equipment status of the enterprise has become a key link in solving the problem of enterprise demand charges.

[0003] In the current relevant technologies, there is a technical problem that the energy storage system cannot dynamically adapt to enterprise load fluctuations and changes in electricity prices during the charging and discharging process. Summary of the invention

[0004] The present application provides a method and device for managing energy storage charging and discharging under load fluctuations, which collects the equipment operation and demand electricity charge data of the enterprise; obtains the enterprise's load fluctuation and electricity price fluctuation data; determines the peak and valley load periods according to the load fluctuation and electricity price fluctuation; generates a charging and discharging strategy for the energy storage system based on these periods; monitors the enterprise's equipment operation, electricity price and energy storage system status in real time to generate optimization constraints; inputs the optimization constraints into the demand electricity charge calculation model to further optimize the charging and discharging strategy, thereby achieving the technical effect of reducing the enterprise's demand electricity charge and improving the efficiency of electricity utilization.

[0005] The present application provides a method for managing energy storage charging and discharging under load fluctuations, including:

[0006] Collect the target enterprise's equipment operation data and demand electricity charge data; obtain the target enterprise's real-time load fluctuation data and electricity price fluctuation data; determine the peak load period and the valley load period based on the real-time load fluctuation data and the electricity price fluctuation data; generate a charge and discharge strategy for the energy storage system based on the peak load period and the valley load period; monitor the target enterprise's equipment operation data, demand electricity charge data, electricity price fluctuation data and the charge and discharge status of the energy storage system in real time to generate charge and discharge optimization constraints; input the charge and discharge optimization constraints into the demand electricity charge calculation model to optimize the charge and discharge strategy.

[0007] The present application also provides an energy storage charging and discharging management device under load fluctuation, including:

[0008] A data acquisition module, the data acquisition module is used to collect the equipment operation data and demand electricity charge data of the target enterprise; an electricity consumption data acquisition module, the electricity consumption data acquisition module is used to obtain the real-time load fluctuation data and electricity price fluctuation data of the target enterprise; a load period determination module, the load period determination module is used to determine the peak load period and the valley load period according to the real-time load fluctuation data and the electricity price fluctuation data; a charge and discharge strategy generation module, the charge and discharge strategy generation module is used to generate a charge and discharge strategy for the energy storage system based on the peak load period and the valley load period; a charge and discharge optimization constraint condition generation module, the charge and discharge optimization constraint condition generation module is used to monitor the equipment operation data, demand electricity charge data, electricity price fluctuation data and the charge and discharge status of the energy storage system of the target enterprise in real time, and generate charge and discharge optimization constraints; a charge and discharge strategy optimization module, the charge and discharge strategy optimization module is used to input the charge and discharge optimization constraints into the demand electricity charge calculation model to optimize the charge and discharge strategy.

[0009] The energy storage charging and discharging management method and device under load fluctuation proposed in this application first collects the equipment operation and demand electricity charge data of the enterprise; obtains the enterprise's load fluctuation and electricity price fluctuation data; determines the peak and valley load periods based on the load fluctuation and electricity price fluctuation; generates the charging and discharging strategy of the energy storage system based on these periods; monitors the equipment operation, electricity price and energy storage system status of the enterprise in real time, and generates optimization constraints; inputs the optimization constraints into the demand electricity charge calculation model, and further optimizes the charging and discharging strategy, thereby achieving the technical effect of reducing the enterprise's demand electricity charges and improving the efficiency of electricity utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A flow chart of a method for managing energy storage charging and discharging under load fluctuations provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of an energy storage charging and discharging management device under load fluctuations provided in an embodiment of the present application.

[0013] Explanation of reference numerals: data acquisition module 10 , power consumption data acquisition module 20 , load period determination module 30 , charge and discharge strategy generation module 40 , charge and discharge optimization constraint condition generation module 50 , charge and discharge strategy optimization module 60 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides a method for managing energy storage charging and discharging under load fluctuations, such as Figure 1 As shown, the method includes:

[0018] Step S100, collect the equipment operation data and demand electricity fee data of the target enterprise. Specifically, first identify the target enterprise, and then collect the equipment operation data. Determine the collection content including equipment operation status, power consumption and operation time, etc. The collection methods include obtaining from the intelligent control system, installing sensors or using a distributed data acquisition system. Then collect the demand electricity fee data, and you need to understand the calculation rules. The data source is mainly the electricity bill, and it can also be obtained from the power supply department system. Through the above steps, the data can be fully and accurately collected to provide a basis for the follow-up.

[0019] Step S200, obtain the real-time load fluctuation data and electricity price fluctuation data of the target enterprise. Specifically, to obtain the real-time load fluctuation data of the target enterprise, it is necessary to first determine the monitoring at the enterprise's incoming line end, select appropriate monitoring equipment, collect data and transmit it to the processing center, analyze the data to extract peak values, valley values ​​and other fluctuation information. For electricity price fluctuation data, it is necessary to obtain it from the power trading market interface or the power supply department's notification, organize and classify the data obtained from the market, and timely update the electricity price data in the notification to provide a basis for the follow-up.

[0020] Step S300, according to the real-time load fluctuation data and the electricity price fluctuation data, determine the peak load period and the valley load period. Specifically, firstly, set the peak and valley load thresholds according to the enterprise's historical electricity consumption data and equipment operation characteristics, then analyze the real-time load fluctuation data to determine the preliminary range of the peak and valley load periods, then combine the electricity price fluctuation data, consider the load and electricity price within the preliminary range, and finally determine the peak load period and the valley load period.

[0021] In a possible implementation, the peak load period and the valley load period are determined according to the real-time load fluctuation data and the electricity price fluctuation data, and step S300 further includes step S310, monitoring the electricity market price fluctuation data of the target enterprise and obtaining the electricity price fluctuation data. Specifically, it is necessary to establish a monitoring mechanism for the price fluctuation data of the electricity market where the target enterprise is located, which involves establishing a connection with an electricity market data provider, or using relevant electricity market monitoring tools to obtain price information of the electricity market in real time, including electricity prices in different time periods (such as peak hours, valley hours, and normal times), as well as the trend of electricity prices over time, and collating and analyzing the obtained price data to determine the fluctuation of electricity prices, that is, to obtain electricity price fluctuation data. For example, if it is found that the electricity price in a certain period of time has increased or decreased compared with the previous benchmark electricity price, such price changes are recorded as part of the electricity price fluctuation data.

[0022] Step S320, based on the equipment operation data of the target enterprise, combined with the electricity price fluctuation data, identify and obtain multiple peak load periods and valley load periods. Specifically, based on the equipment operation data of the target enterprise, the analysis is performed. The equipment operation data includes information such as the start and stop time of the equipment and the power consumption of the equipment. By analyzing the data, the electricity load of the enterprise in different time periods can be understood. Combined with the obtained electricity price fluctuation data, the equipment operation data and the electricity price are considered at the same time to identify the peak load period and valley load period. For the peak load period, when the equipment operation data shows that the enterprise's electricity load is high, and the corresponding electricity price fluctuation data shows that the electricity price is also at a high level at this time, the period can be identified as the peak load period. For example, if the power consumption of a company's production equipment is high between 3pm and 5pm, and the electricity price during this period is also a high-price period of the day, then 3pm-5pm is identified as a peak load period. For the valley load period, when the equipment operation data shows that the company's power load is low, and the electricity price fluctuation data shows that the electricity price is at a low level at this time, the period can be identified as a valley load period. For example, if the company's equipment power consumption is very low between 2am and 4am, and the electricity price during this period is a low-price period, then 2am-4am is identified as a valley load period. In this way, multiple peak load periods and valley load periods can be identified.

[0023] Step S400, based on the peak load period and the valley load period, generate a charge and discharge strategy for the energy storage system. Specifically, during the valley load period, first determine the charging start conditions of the energy storage system, including its own state and the power supply capacity of the power grid, and then determine the charging power by comprehensively considering the capacity of the energy storage system, the state of charge, and the power load of the enterprise. During the peak load period, first determine the discharge start conditions, including the amount of electricity and the discharge capacity, and then determine the discharge power by comprehensively considering the power demand of the enterprise and the characteristics of the energy storage system, thereby generating a charge and discharge strategy suitable for the energy storage system.

[0024] In a possible implementation, based on the peak load period and the valley load period, a charging and discharging strategy of the energy storage system is generated, and step S400 further includes step S410, in which the energy storage system is started to store electricity during the valley load period. Specifically, first, it is necessary to accurately judge the valley load period, based on the previous analysis of the target enterprise's real-time load fluctuation data and the electricity price floating data. When it is determined to be in the valley load period, the system will detect the current state of the energy storage system and check whether the energy storage system is in a chargeable state, including checking whether the connection of the energy storage system is normal, whether there is a fault alarm, etc. If the energy storage system is in a normal state, an instruction to start charging will be sent to the energy storage system. After receiving the instruction, the energy storage system will start charging according to a preset charging strategy. The charging strategy may be determined based on factors such as the capacity of the energy storage system, the current power, and the power supply capacity of the power grid during the valley period. For example, if the energy storage system has a large capacity and a low current power, and the power supply capacity of the power grid is sufficient, the energy storage system may be charged at a higher power to store more electricity as soon as possible.

[0025] Step S420, during the peak load period, the energy storage system is started for discharge. Specifically, the peak load period is also determined through preliminary analysis. When entering the peak load period, the system will detect the state of the energy storage system again to confirm whether the power of the energy storage system is sufficient for discharge. If the power meets the requirements and the energy storage system is fault-free, a discharge instruction will be sent to the energy storage system. After receiving the instruction, the energy storage system starts to discharge according to the preset discharge strategy. The discharge strategy may consider factors such as the enterprise's electricity demand during the peak period and the energy storage system's own discharge capacity. For example, if the enterprise has a large electricity demand during the peak period and the energy storage system's discharge capacity allows, the energy storage system may discharge at a higher power to meet part of the enterprise's electricity demand and reduce the amount of electricity the enterprise obtains from the power grid.

[0026] Step S430: optimizing the charging and discharging power of the energy storage system according to the demand electricity fee during the peak load period. Specifically, first, obtain the demand charge data during the peak load period, which can be obtained through the company's electricity bill or real-time data interaction with the power supply department, and analyze the relationship between the demand charge and the charging and discharging power of the energy storage system. If it is found that the current charging and discharging power setting of the energy storage system causes the demand charge to be too high, for example, because the discharge power is insufficient, the company still needs to obtain a large amount of electricity from the power grid, thereby increasing the demand charge, it is necessary to adjust the charging and discharging power. When adjusting the charging and discharging power, multiple factors must be considered comprehensively. On the one hand, the actual electricity demand of the company during the peak period must be considered, and the normal production of the company must not be affected by excessive reduction of the discharge power; on the other hand, the inherent characteristics of the energy storage system must be considered, such as the maximum charging power and maximum discharging power limits. By reasonably adjusting the charging and discharging power, the energy storage system can reduce the demand charge as much as possible while meeting the company's electricity demand. For example, if it is found that increasing the discharge power can reduce the amount of electricity the company obtains from the power grid, and does not exceed the maximum discharge power limit of the energy storage system, while also meeting the company's electricity demand, the discharge power can be appropriately increased.

[0027] In a possible implementation, the charging and discharging power of the energy storage system is optimized according to the demand electricity charge during the peak load period, and step S430 further includes step S431, obtaining the equipment operation data of the target enterprise, and the equipment operation data includes the real-time power consumption data of the equipment, the load data of the equipment, and the start-up and shutdown time log data of the equipment. Specifically, first, determine the method and equipment for data acquisition. For the real-time power consumption data of the equipment, it can be obtained by installing a power sensor on the power line of the equipment. The sensor can measure the power consumption of the equipment in real time and transmit the data to the data acquisition system. The equipment load data can be obtained from the control system of the equipment itself. If the equipment has relevant load monitoring functions, or the load of the equipment is inferred by analyzing the operating status and power consumption data of the equipment, the start-up and shutdown time log data of the equipment can be obtained through the recording function of the control system of the equipment, or a monitoring device is installed on the startup and shutdown circuit of the equipment to record the time of each startup and shutdown of the equipment, and the data is summarized in the data acquisition system.

[0028] Step S432, analyze the historical equipment operation data to identify the peak power demand period and peak power demand duration. Specifically, the collected historical equipment operation data is sorted and classified, and the real-time power consumption data of the equipment, the equipment load data, and the equipment startup and shutdown time log data are arranged in chronological order to form a complete equipment operation history data set. The historical data set is analyzed through data analysis algorithms and tools. Time series analysis methods, such as moving average method, autoregressive integrated moving average model (ARIMA), etc., can be used to identify the peak power demand period during the equipment operation process. These time periods are usually time periods when the equipment power consumption is high and lasts for a period of time. The duration of each peak power demand period is determined, and the peak power demand duration is determined by analyzing the length of the time period in which the equipment power consumption in the data set is higher than a certain threshold.

[0029] Step S433, based on the peak power demand period and the peak power demand duration, calculate the predicted peak power demand of the target enterprise. Specifically, for each identified peak power demand period, calculate the average power consumption of the equipment in the period, and the average power consumption can be obtained by summing the real-time power consumption data of the equipment in the period and dividing it by the duration of the period. According to the peak power demand duration and the average power consumption, calculate the predicted peak power demand. The predicted peak power demand can be obtained by multiplying the average power consumption by a coefficient. This coefficient can be adjusted according to the equipment operation characteristics and historical data of the enterprise. For example, if the power consumption of the enterprise's equipment fluctuates greatly during the peak period, this coefficient can be appropriately increased to ensure that the predicted peak power demand can cover the higher power demand that may occur.

[0030] Step S434: dynamically adjusting the charging and discharging power of the energy storage system according to the predicted peak power demand and the demand electricity fee during the peak load period. Specifically, first, obtain the demand charge information during the peak load period. This can be obtained from the company's electricity bill or real-time data interaction with the power supply department. Compare the predicted peak power demand and the demand charge during the peak load period. If the predicted peak power demand is high, and the current energy storage system charging and discharging power setting may cause the company to pay a higher demand charge during the peak load period, it is necessary to adjust the charging and discharging power. When dynamically adjusting the charging and discharging power, multiple factors must be considered. On the one hand, the actual power demand of the company during the peak load period must be considered, and the normal production of the company must not be affected by excessive reduction of the discharge power; on the other hand, the inherent characteristics of the energy storage system must be considered, such as the maximum charging power and maximum discharging power limits. If the predicted peak power demand is high and the demand charge is also high, the discharge power of the energy storage system can be appropriately increased, provided that it does not exceed the maximum discharge power limit of the energy storage system, and ensure that the company can obtain sufficient power supply during the peak load period. If the company's power demand during the peak load period is low, but the demand charge is still high, the charging power of the energy storage system can be appropriately reduced to reduce the company's demand charge expenditure during the peak load period.

[0031] Step S500, monitor the equipment operation data, demand electricity charge data, electricity price fluctuation data and the charge and discharge status of the energy storage system of the target enterprise in real time, and generate charge and discharge optimization constraints. Specifically, monitor the equipment operation data (including power consumption, load, start-up and shutdown time log, etc.), demand electricity charge data, electricity price fluctuation data and the charge and discharge status (electricity, power, temperature, etc.) of the target enterprise in real time. Analyze the changes in equipment operation data, pay attention to the demand electricity charge and electricity price fluctuation trends, and the rationality of the energy storage system status. Based on these, if the equipment power increases or the load fluctuates, adjust the charge and discharge power; if the demand electricity charge rises, reduce the charging power at peak and increase the charging power at valley; if the peak electricity price rises, increase the discharge power at peak; if the valley electricity price drops, increase the charging power at valley; if the energy storage system is low in power, limit the discharge power and give priority to charging; if the high charging power affects the battery life, reduce the charging power, thereby generating charge and discharge optimization constraints.

[0032] Step S600, input the charging and discharging optimization constraints into the demand electricity charge calculation model to optimize the charging and discharging strategy. Specifically, first understand the model based on the enterprise's demand electricity charge calculation rules, whose input includes data such as electricity load, and the output is the demand electricity charge value. Then input the charging and discharging optimization constraints (such as charging and discharging power limits in different time periods, electricity load limits, etc.) into the model, and the model will simulate and compare the demand electricity charges under different strategies. If the current strategy is not optimal, adjust the charging and discharging strategy, such as changing the power or equipment operating time during peak and valley periods, and repeat this process until the optimal strategy is found to minimize the demand electricity charge.

[0033] In one possible implementation, the charging and discharging optimization constraint condition is input into the demand electricity charge calculation model to optimize the charging and discharging strategy. Step S600 further includes step S610. If the available power of the energy storage system is higher than a preset threshold value and during the peak load period, the energy storage system is discharged preferentially. Specifically, first, the available power of the energy storage system must be monitored in real time. This is achieved through the energy storage system's own power monitoring module, which can accurately measure the power currently stored in the energy storage system and compare the monitored available power with the preset threshold value. The preset threshold value is pre-set based on factors such as the capacity of the energy storage system, the enterprise's electricity demand, and the requirements for the performance of the energy storage system. For example, if the capacity of the energy storage system is 1,000 kWh, based on experience and analysis, the preset threshold value may be set to 600 kWh. When it is determined to be in a peak load period (this is determined by previously analyzing the real-time fluctuation data of the enterprise's load and the fluctuation of electricity prices), the energy storage system will be discharged first. When the available power of the energy storage system is higher than the preset threshold, the system will send a discharge command to the energy storage system. After receiving the discharge command, the energy storage system will start discharging according to its own discharge capacity and the enterprise's electricity demand. The determination of the discharge power should take into account the maximum discharge power limit of the energy storage system and the actual electricity demand of the enterprise during the peak load period. For example, if the enterprise's electricity demand during the peak load period is 800 kilowatts and the maximum discharge power of the energy storage system is 600 kilowatts, then the discharge power can be set to 600 kilowatts. At the same time, the enterprise also needs to obtain 200 kilowatts of electricity from the power grid.

[0034] Step S620: If the available power of the energy storage system is lower than a preset threshold and it is during the off-peak load period, the energy storage system is charged first. Specifically, the available power of the energy storage system should be monitored in real time. When it is detected that the available power of the energy storage system is lower than the preset threshold, it is determined whether it is in the valley load period. The determination of the valley load period is also based on the previous analysis. When it is determined that it is in the valley load period and the available power of the energy storage system is lower than the preset threshold, the system will check whether the connection of the energy storage system is normal and the power supply capacity of the power grid during the valley period. If the energy storage system is connected normally and the power supply capacity of the power grid allows, the system will send a charging instruction to the energy storage system. After receiving the charging instruction, the energy storage system will start charging according to its own charging capacity and the power supply capacity of the power grid during the valley period. The determination of the charging power should comprehensively consider the maximum charging power limit of the energy storage system and the remaining power supply capacity of the power grid during the valley period. For example, if the power supply capacity of the power grid during the valley period is 500 kilowatts and the maximum charging power of the energy storage system is 400 kilowatts, then the charging power can be set to 400 kilowatts. At the same time, if the enterprise still has a certain power demand during the valley load period, such as 200 kilowatts, then the actual charging power provided by the power grid to the energy storage system is 300 kilowatts (500-200).

[0035] In a possible implementation, the charging and discharging optimization constraint condition is input into a demand electricity cost calculation model to optimize the charging and discharging strategy. Step S600 further includes step S630, calculating the demand electricity cost change trend of the target enterprise through the demand electricity cost calculation model. Specifically, first, the demand charge calculation model is constructed based on the calculation rules of the enterprise's demand charge, taking into account the enterprise's electricity load curve (usually a 15-minute load curve), the billing method of the demand charge (for example, billing based on the maximum value of the load curve), and other possible adjustment factors (such as power factor adjustment, etc.). The enterprise's historical electricity consumption data (including electricity load data in different time periods), the historical charging and discharging data of the energy storage system (such as the charging and discharging power in each time period), and related electricity price data are provided as input parameters to the demand charge calculation model. The model performs complex calculations and analyses on the input data to obtain the enterprise's demand charge values ​​in different time periods. Then, by performing time series analysis on these values, the changing trend of the demand charge can be calculated. For example, if the enterprise's demand charge shows an upward trend for several consecutive months, or if there is a clear increasing trend in the demand charge during certain specific time periods (such as the peak electricity consumption period in summer), these can be obtained through model calculation and analysis.

[0036] Step S640, dynamically adjust the charging and discharging strategy of the energy storage system according to the changing trend of the demand electricity charge. Specifically, after determining the changing trend of the demand electricity charge, if it is found that the demand electricity charge is on an upward trend, it is necessary to adjust the charging and discharging strategy of the energy storage system. For example, if the demand electricity charge increases significantly during the peak load period, it may be necessary to consider increasing the discharge power of the energy storage system during the peak load period, because increasing the discharge power can reduce the amount of electricity that the enterprise obtains from the power grid during the peak period, thereby possibly reducing the demand electricity charge. At the same time, it is necessary to consider the inherent characteristics of the energy storage system, such as the maximum discharge power limit and the actual electricity demand of the enterprise during the peak period. If the enterprise's electricity demand during the peak period is 800 kilowatts, and the maximum discharge power of the energy storage system is 600 kilowatts, then the discharge power can be appropriately increased, but it cannot exceed 600 kilowatts. If the demand electricity charge changes The trend shows that demand charges are also affected during off-peak load periods. For example, if the demand charge rises because the energy storage system's charging power is too high (perhaps because charging increases the load, causing the maximum value of the load curve to increase), then the charging strategy during the off-peak load period needs to be adjusted. The charging power may need to be reduced, or the charging time may need to be adjusted to avoid an adverse impact on the demand charge. If the demand charge change trend shows that the demand charge is relatively stable and at a reasonable level during certain periods (such as a specific period in the transition season), then the current charging and discharging strategy can be maintained, but continuous monitoring is still required to prevent changes. According to the change trend calculated by the demand charge calculation model, the charging and discharging strategy of the energy storage system is dynamically adjusted to achieve the optimization of the enterprise's demand charge.

[0037] In a possible implementation, the charging and discharging optimization constraint condition is input into a demand electricity charge calculation model to optimize the charging and discharging strategy. Step S600 further includes step S650, monitoring the battery health status of the energy storage system to obtain battery health status data. Specifically, first, we need to determine the methods and equipment for monitoring the battery health status. For lithium battery energy storage systems, common monitoring methods include measuring battery voltage, current, temperature and other parameters. Corresponding sensors, such as voltage sensors, current sensors and temperature sensors, can be installed in the battery pack. The sensors will collect relevant data of the battery in real time and transmit the data to the data processing unit. For example, the voltage sensor will accurately measure the terminal voltage of the battery, the current sensor will measure the current size during the battery charging and discharging process, and the temperature sensor will monitor the operating temperature of the battery. By analyzing and processing the collected data, the battery health status data can be obtained. The data includes the remaining capacity estimation of the battery, the internal resistance measurement value of the battery, the battery health index (such as the battery health status expressed as a percentage), etc. For example, by analyzing the voltage and current change curves of the battery, the remaining capacity of the battery can be estimated; by measuring the change in the internal resistance of the battery, the degree of aging of the battery can be judged, and the battery health index can be obtained.

[0038] Step S660, based on the battery health status data, the energy storage system is updated. Specifically, after the battery health status data is obtained, the energy storage system is updated accordingly according to the data. If the battery health status data shows that the remaining capacity of the battery is low, it may be necessary to adjust the charging and discharging strategy of the energy storage system, for example, reduce the depth of discharge and avoid over-discharge to extend the service life of the battery. If the internal resistance of the battery increases, it indicates that the battery is aged to a high degree, and it may be necessary to consider replacing the battery pack or maintaining the battery. For example, professionals may be arranged to perform maintenance operations such as balanced charging and repair on the battery, or replace part or all of the battery packs when appropriate. If the battery health status data reflects that the battery performance has significantly decreased under certain specific conditions (such as high temperature environment), it may be necessary to improve the operating environment of the energy storage system, for example, add heat dissipation equipment to ensure that the battery operates within a suitable temperature range, thereby improving the performance and service life of the battery. The battery health status of the energy storage system is monitored and updated according to the data to ensure the efficient operation of the energy storage system and the service life of the battery.

[0039] The embodiment of the present application collects the equipment operation and demand electricity charge data of the enterprise; obtains the enterprise's load fluctuation and electricity price fluctuation data; determines the peak and valley load periods according to the load fluctuation and electricity price fluctuation; generates the charging and discharging strategy of the energy storage system based on these periods; monitors the equipment operation, electricity price and energy storage system status of the enterprise in real time, and generates optimization constraints; inputs the optimization constraints into the demand electricity charge calculation model, and further optimizes the charging and discharging strategy, thereby achieving the technical effect of reducing the enterprise's demand electricity charge and improving the efficiency of electricity utilization.

[0040] In the above, refer to Figure 1 The energy storage charging and discharging management method under load fluctuation according to an embodiment of the present invention is described in detail. Figure 2 An energy storage charge and discharge management device under load fluctuation according to an embodiment of the present invention is described.

[0041] The energy storage charge and discharge management device under load fluctuation according to the embodiment of the present invention is used to solve the technical problem that the existing energy storage system cannot dynamically adapt to the enterprise load fluctuation and electricity price changes during the charge and discharge process, and achieves the technical effect of reducing the enterprise demand electricity charges and improving the power utilization efficiency. The energy storage charge and discharge management device under load fluctuation includes: a data acquisition module 10, an electricity consumption data acquisition module 20, a load period determination module 30, a charge and discharge strategy generation module 40, a charge and discharge optimization constraint condition generation module 50, and a charge and discharge strategy optimization module 60.

[0042] The data collection module 10 is used to collect the equipment operation data and demand electricity fee data of the target enterprise.

[0043] The electricity consumption data acquisition module 20 is used to acquire the real-time load fluctuation data and electricity price fluctuation data of the target enterprise.

[0044] The load period determination module 30 is used to determine the peak load period and the valley load period according to the real-time load fluctuation data and the electricity price fluctuation data.

[0045] The charge and discharge strategy generation module 40 is used to generate a charge and discharge strategy for the energy storage system based on the peak load period and the valley load period.

[0046] The charge-discharge optimization constraint condition generation module 50 is used to monitor the target enterprise's equipment operation data, demand electricity fee data, electricity price fluctuation data and the charge-discharge status of the energy storage system in real time, and generate charge-discharge optimization constraint conditions.

[0047] The charge-discharge strategy optimization module 60 is used to input the charge-discharge optimization constraint conditions into the demand electricity fee calculation model to optimize the charge-discharge strategy.

[0048] The present application provides a method and device for managing energy storage charging and discharging under load fluctuations, such as Figure 1 As shown, the method includes:

[0049] The specific configuration of the load period determination module 30 will be described in detail below. As described above, the peak load period and the valley load period are determined according to the real-time load fluctuation data and the electricity price fluctuation data. The load period determination module 30 further includes: an electricity price fluctuation data acquisition unit, which is used to monitor the power market price fluctuation data of the target enterprise and obtain the electricity price fluctuation data; a load period identification unit, which is used to identify and obtain multiple peak load periods and valley load periods based on the equipment operation data of the target enterprise and the electricity price fluctuation data.

[0050] The specific configuration of the charge-discharge strategy generation module 40 will be described in detail below. As described above, based on the peak load period and the valley load period, the charge-discharge strategy of the energy storage system is generated, and the charge-discharge strategy generation module 40 further includes: a power storage unit, which is used to start the energy storage system to store power during the valley load period; a start-discharge unit, which is used to start the energy storage system to discharge during the peak load period; and a charge-discharge power optimization unit, which is used to optimize the charge-discharge power of the energy storage system according to the demand electricity charge during the peak load period.

[0051] Among them, the charging and discharging power optimization unit is used to optimize the charging and discharging power of the energy storage system according to the demand electricity charge during the peak load period. The charging and discharging power optimization unit further includes: an equipment operation data acquisition subunit, the equipment operation data acquisition subunit is used to acquire the equipment operation data of the target enterprise, the equipment operation data includes real-time power consumption data of the equipment, equipment load data, and equipment startup and shutdown time log data; a power demand data acquisition subunit, the power demand data acquisition subunit is used to analyze historical equipment operation data, identify the peak power demand period and the peak power demand duration; a predicted peak power demand calculation subunit, the predicted peak power demand calculation subunit is used to calculate the predicted peak power demand of the target enterprise based on the peak power demand period and the peak power demand duration; a charging and discharging power adjustment subunit, the charging and discharging power adjustment subunit is used to dynamically adjust the charging and discharging power of the energy storage system according to the predicted peak power demand and the demand electricity charge during the peak load period.

[0052] The specific configuration of the charge-discharge strategy optimization module 60 will be described in detail below. As described above, the charge-discharge optimization constraint condition is input into the demand electricity charge calculation model to optimize the charge-discharge strategy. The charge-discharge strategy optimization module 60 further includes: a priority discharge unit, wherein the priority discharge unit is used to discharge the energy storage system if the available power of the energy storage system is higher than a preset threshold value and during the peak load period. A priority charging unit, wherein the priority charging unit is used to charge the energy storage system if the available power of the energy storage system is lower than a preset threshold value and during the valley load period.

[0053] Among them, the charging and discharging strategy optimization module 60 further includes: a demand electricity charge change trend calculation unit, which is used to calculate the demand electricity charge change trend of the target enterprise through the demand electricity charge calculation model; a charging and discharging strategy dynamic adjustment unit, which is used to dynamically adjust the charging and discharging strategy of the energy storage system according to the demand electricity charge change trend.

[0054] Among them, the charging and discharging strategy optimization module 60 further includes: a battery health status data acquisition unit, which is used to monitor the battery health status of the energy storage system and obtain battery health status data; an energy storage system update unit, which is used to update the energy storage system based on the battery health status data.

[0055] The energy storage charge and discharge management device under load fluctuation provided in the embodiment of the present invention can execute the energy storage charge and discharge management method under load fluctuation provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0056] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0057] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for managing energy storage charging and discharging under load fluctuations, characterized in that: include: Collect equipment operation data and demand electricity fee data of target enterprises; Obtaining real-time load fluctuation data and electricity price fluctuation data of the target enterprise; Determine a peak load period and a valley load period according to the real-time load fluctuation data and the electricity price fluctuation data; Based on the peak load period and the valley load period, generating a charging and discharging strategy for the energy storage system; Real-time monitoring of the target enterprise's equipment operation data, demand electricity charge data, electricity price fluctuation data, and the charging and discharging status of the energy storage system to generate charging and discharging optimization constraint conditions; The charging and discharging optimization constraint conditions are input into a demand electricity fee calculation model to optimize the charging and discharging strategy.

2. The energy storage charging and discharging management method under load fluctuation according to claim 1 is characterized in that: The step of determining a peak load period and a valley load period according to the real-time load fluctuation data and the electricity price fluctuation data comprises: Monitoring the electricity market price fluctuation data of the target enterprise and obtaining the electricity price fluctuation data; Based on the equipment operation data of the target enterprise and in combination with the electricity price fluctuation data, multiple peak load periods and valley load periods are identified and obtained.

3. The energy storage charging and discharging management method under load fluctuation according to claim 1 is characterized in that: The generating a charging and discharging strategy of the energy storage system based on the peak load period and the valley load period includes: During the off-peak load period, starting the energy storage system to store electricity; During the peak load period, starting the energy storage system to discharge; The charging and discharging power of the energy storage system is optimized according to the demand electricity fee during the peak load period.

4. The energy storage charging and discharging management method under load fluctuation according to claim 3 is characterized in that: The optimizing the charging and discharging power of the energy storage system according to the demand electricity fee during the peak load period includes: Acquire the equipment operation data of the target enterprise, wherein the equipment operation data includes real-time power consumption data of the equipment, equipment load data, and equipment startup and shutdown time log data; Analyze historical equipment operation data to identify peak power demand periods and durations; Calculate the predicted peak power demand of the target enterprise based on the peak power demand period and peak power demand duration; The charging and discharging power of the energy storage system is dynamically adjusted according to the predicted peak power demand and the demand electricity fee during the peak load period.

5. The energy storage charging and discharging management method under load fluctuation according to claim 1, characterized in that: The optimizing the charging and discharging strategy includes: If the available power of the energy storage system is higher than a preset threshold and during the peak load period, the energy storage system is discharged first; If the available power of the energy storage system is lower than a preset threshold and during the off-peak load period, the energy storage system is charged first.

6. The energy storage charging and discharging management method under load fluctuation according to claim 5 is characterized in that: Inputting the charging and discharging optimization constraint condition into the demand electricity fee calculation model to optimize the charging and discharging strategy includes: Calculating the demand electricity fee change trend of the target enterprise through the demand electricity fee calculation model; According to the changing trend of the demand electricity charge, the charging and discharging strategy of the energy storage system is dynamically adjusted.

7. The energy storage charging and discharging management method under load fluctuation according to claim 5, characterized in that: include: Monitoring the battery health status of the energy storage system to obtain battery health status data; Based on the battery health status data, the energy storage system is updated.

8. An energy storage charging and discharging management device under load fluctuation, characterized in that: The device is used to implement the energy storage charging and discharging management method under load fluctuation according to any one of claims 1 to 7, and the device comprises: A data collection module, which is used to collect equipment operation data and demand electricity fee data of the target enterprise; An electricity consumption data acquisition module, which is used to acquire real-time load fluctuation data and electricity price fluctuation data of the target enterprise; A load period determination module, the load period determination module is used to determine a peak load period and a valley load period according to the real-time load fluctuation data and the electricity price fluctuation data; A charging and discharging strategy generation module, the charging and discharging strategy generation module is used to generate a charging and discharging strategy for the energy storage system based on the peak load period and the valley load period; A charge-discharge optimization constraint condition generation module, which is used to monitor the equipment operation data, demand electricity fee data, electricity price fluctuation data of the target enterprise and the charge-discharge status of the energy storage system in real time to generate charge-discharge optimization constraint conditions; A charge and discharge strategy optimization module, wherein the charge and discharge strategy optimization module is used to input the charge and discharge optimization constraint conditions into a demand electricity fee calculation model to optimize the charge and discharge strategy.

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