Power supply guarantee and consumption optimization regulation method and device for multi-energy storage collaborative operation

Abstract

The application relates to the technical field of power system regulation, and discloses a power supply guarantee and consumption optimization regulation method and device for multi-element energy storage collaborative operation, which comprises the following steps: according to historical power fluctuation data of an energy storage system in grid operation, the quantiles of short-time power demand and long-time power demand are counted as initial upper limit values of energy storage charging and discharging power; a disturbance interval is generated in combination with power up and down fluctuation amplitudes; the initial upper limit values are randomly disturbed; a preset multi-element energy storage scheduling model and candidate upper limit values are used to simulate the operation of short-time disturbance and long-time peak shaving of the grid; the grid frequency deviation and the energy storage device health attenuation rate are calculated; the target upper limit values are screened out; and the target upper limit values of the energy storage charging and discharging power are used to regulate the power supply guarantee and consumption of the multi-element energy storage collaborative operation. The application finds reasonable upper limits of the energy storage charging and discharging power, improves the grid regulation capacity, alleviates the energy storage life attenuation, and effectively balances the power supply guarantee and new energy consumption.

Description

Technical Field

[0001] This invention relates to the field of power system regulation technology, and in particular to a method and device for power supply guarantee and absorption optimization regulation with multi-energy storage coordinated operation. Background Technology

[0002] With the accelerated global energy transition, the proportion of new energy sources such as wind and solar power in the power system is increasing year by year. However, the output of new energy sources is significantly random and volatile, and large-scale integration can easily lead to problems such as grid voltage fluctuations, frequency deviations, and supply-demand imbalances. At the same time, the peak-valley difference in user-side load is gradually widening, and traditional peak-shaving methods are insufficient to meet the dual demands of high-proportion new energy consumption and power supply security.

[0003] Multi-element energy storage systems, as an integrated power regulation resource, can simultaneously utilize various energy storage units such as electrochemical energy storage, supercapacitors, and flywheel energy storage. Through complementary advantages, they achieve rapid response, long-term support, and efficient energy management, thus playing a crucial role in power supply security and the optimization of new energy consumption. In existing technologies, the coordinated operation of multi-element energy storage systems mainly relies on power allocation, energy management, and dispatch strategies. However, the operating boundary conditions of the energy storage units, especially the setting of the upper limit of charging and discharging power, have a direct impact on the overall system performance and operational efficiency.

[0004] Specifically, if the upper limit of energy storage charging and discharging power is set too high, although it can improve the response to grid fluctuations and enhance the absorption capacity of new energy fluctuations in the short term, it will also cause energy storage units such as batteries to frequently be in a high-current charging and discharging state, thereby leading to problems such as accelerated lifespan degradation and increased safety risks, weakening the long-term stability of the system. Conversely, if the upper limit of energy storage charging and discharging power is set too low, the peak-shaving and frequency regulation capabilities of multi-energy storage on the grid side will be insufficient, making it difficult to provide timely support during grid disturbances. At the same time, it will be unable to effectively absorb new energy sources when they are at high output, resulting in insufficient absorption of wind and solar power.

[0005] Therefore, how to reasonably determine and adjust the upper limit of energy storage charging and discharging power during the coordinated operation of multiple energy storage systems, so as to achieve a balance between equipment life protection and grid operation needs, is not only related to the safe and stable operation of the power system, but also directly affects the absorption level of new energy and the utilization efficiency of energy storage investment. It is a key technical problem that urgently needs to be solved in this field.

[0006] In the existing multi-energy storage collaborative operation, the upper limit of energy storage charging and discharging power is set unreasonably, which can easily lead to insufficient grid regulation capacity or accelerated decay of energy storage life, making it difficult to balance the issues of power supply guarantee and new energy consumption. Summary of the Invention

[0007] This invention provides a method and device for optimizing and regulating power supply and consumption through the coordinated operation of multiple energy storage systems. Its main purpose is to solve the problem that unreasonable setting of the upper limit of energy storage charging and discharging power can easily lead to insufficient grid regulation capacity or accelerated decay of energy storage life, making it difficult to balance power supply security and new energy consumption.

[0008] Firstly, to achieve the above objectives, the present invention provides a method for power supply security and optimized regulation through the coordinated operation of multiple energy storage systems, comprising:

[0009] Acquire historical power fluctuation data of the energy storage system during grid operation, and generate short-term and long-term power demand based on the historical power fluctuation data;

[0010] The quantiles of the short-term power demand and the long-term power demand are calculated, and these quantiles are used as the initial upper limit of the energy storage charging and discharging power.

[0011] Calculate the power fluctuation range of the historical power fluctuation data, and generate a disturbance range based on the power fluctuation range and the initial upper limit value;

[0012] The initial upper limit value is randomly perturbed using the perturbation interval to obtain several candidate upper limit values;

[0013] The short-term disturbance and long-term peak shaving of the power grid are simulated using a preset multi-element energy storage scheduling model and the candidate upper limit value. The power grid frequency deviation and the health degradation rate of the energy storage equipment are calculated based on the simulation process.

[0014] Based on the grid frequency deviation and the health degradation rate of the energy storage device, a target upper limit value is selected from the candidate upper limit values, and the target upper limit value of the energy storage charging and discharging power is used to ensure power supply and regulate the absorption of multiple energy storage systems in coordinated operation.

[0015] Secondly, the present invention also provides a power supply guarantee and consumption regulation device with multi-energy storage coordination, the device comprising:

[0016] The power demand generation module is used to acquire historical power fluctuation data of the energy storage system during grid operation, and generate short-term power demand and long-term power demand based on the historical power fluctuation data.

[0017] An initial upper limit generation module is used to calculate the quantiles of the short-term power demand and the long-term power demand, and to use the quantiles as the initial upper limit of the energy storage charging and discharging power.

[0018] The disturbance interval generation module is used to calculate the power fluctuation amplitude of the historical power fluctuation data and generate a disturbance interval based on the power fluctuation amplitude and the initial upper limit value.

[0019] The candidate upper limit value generation module is used to randomly perturb the initial upper limit value using the perturbation interval to obtain several candidate upper limit values.

[0020] The candidate upper limit simulation module is used to simulate the operation of the power grid under short-term disturbances and long-term peak shaving using a preset multi-element energy storage scheduling model and the candidate upper limit value, and to calculate the power grid frequency deviation and the health degradation rate of the energy storage device based on the simulation process.

[0021] The target upper limit application module is used to select a target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device, and to use the target upper limit value of the energy storage charging and discharging power to ensure power supply and regulate the power consumption of the multi-energy storage collaborative operation.

[0022] Thirdly, the present invention also provides an electronic device, the electronic device comprising:

[0023] At least one processor; and,

[0024] A memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the aforementioned method for power supply guarantee and consumption optimization regulation through the coordinated operation of multiple energy storage systems.

[0026] This invention generates an initial upper limit value by combining the statistical characteristics of historical power fluctuations and introduces a random disturbance mechanism to produce candidate solutions. This overcomes the limitations of traditional methods that rely on fixed values ​​or empirical thresholds, making the power upper limit setting more closely aligned with the actual operational needs of the power grid. Through operational simulation, two key indicators—grid frequency stability (frequency deviation) and energy storage device durability (health degradation rate)—are simultaneously evaluated. Based on these, a target upper limit value is selected, effectively delaying the aging of energy storage devices while ensuring the safe and stable operation of the power grid, thus balancing system performance and device lifespan. Through coordinated optimization for short-term disturbances and long-term peak-shaving scenarios, the determined target upper limit value can guide diversified energy storage to provide precise power support and energy transfer at different time scales, significantly improving the grid's adaptability and absorption capacity to renewable energy output fluctuations and load changes. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a method for ensuring power supply and optimizing power consumption through the coordinated operation of multiple energy storage systems, as provided in an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of a multi-energy storage synergistic power guarantee and consumption regulation device provided in an embodiment of the present invention.

[0030] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0031] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0033] This application provides a method for power supply guarantee and consumption optimization regulation based on the coordinated operation of multiple energy storage systems. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the device provided in this application: a server, a terminal, etc. In other words, the method for power supply guarantee and consumption optimization regulation based on the coordinated operation of multiple energy storage systems can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0034] Reference Figure 1 The diagram shown is a flowchart illustrating a method for ensuring and optimizing power supply through the coordinated operation of multiple energy storage systems, according to an embodiment of the present invention. In this embodiment, the method for ensuring and optimizing power supply through the coordinated operation of multiple energy storage systems includes:

[0035] S1. Obtain historical power fluctuation data of the energy storage system during grid operation, and generate short-term power demand and long-term power demand based on the historical power fluctuation data.

[0036] In this embodiment of the invention, the energy storage system is a complete device consisting of energy storage equipment, power conversion devices, an energy management system, and auxiliary facilities. It is capable of storing and releasing electrical energy during grid operation and, through unified management and control, undertakes functions such as peak shaving, frequency regulation, peak shaving and valley filling, and renewable energy consumption. Grid operation refers to the process by which the power system maintains a real-time balance between power supply and consumption, including power generation, transmission, distribution, and load absorption. Historical power fluctuation data refers to records of power changes over a period of time, reflecting the characteristics of power fluctuations over time.

[0037] Short-term power demand refers to fluctuations in electricity demand over a short timescale (e.g., minutes, hours). It is mainly used to address rapidly fluctuating load changes or instantaneous fluctuations in renewable energy generation, such as sudden fluctuations in wind and solar power generation. Long-term power demand refers to the trend of electricity demand changes over a longer timescale (e.g., days, weeks, or seasons). It is mainly used to address slowly changing loads or renewable energy absorption issues, such as daily peak-valley regulation and seasonal load changes.

[0038] Specifically, generating short-term and long-term power demands based on the historical power fluctuation data includes:

[0039] Standard power fluctuation data is obtained by uniformizing the time step of the historical power fluctuation data;

[0040] The standard power fluctuation data is subjected to short-time filtering to obtain filtered power fluctuation data.

[0041] Extract the high-frequency fluctuation component of the filtered power fluctuation data;

[0042] Short-term power demand is generated based on the high-frequency fluctuation component;

[0043] The standard power fluctuation data is decomposed into low-frequency trends to obtain the low-frequency fluctuation component;

[0044] Long-term energy variation and daily load curves are generated based on the low-frequency fluctuation portion;

[0045] The long-term energy variation and the daily load curve are summarized into long-term power demand.

[0046] In detail, historical power fluctuation data comes from devices with different sampling frequencies, such as some recording every minute and some every second. Converting these data into the same time interval (e.g., one data point per minute) results in standard power fluctuation data, which is a continuous power data sequence at a fixed time interval.

[0047] Short-time filtering is a process that smooths power fluctuation data, removing very sharp noise or transient anomalies. Common methods include moving average filtering and wavelet filtering. The resulting filtered power fluctuation data can more accurately reflect short-term power change trends without being disturbed by sudden anomalies.

[0048] High-frequency fluctuations refer to the rapidly changing portion of power over time, corresponding to short-term power fluctuations, such as load changes or fluctuations in renewable energy generation lasting from a few seconds to a few minutes. Extracting high-frequency fluctuations typically involves differential, Fourier transform, or filter separation methods to isolate the rapidly changing components. The extracted high-frequency fluctuations are then used directly as the basis for short-term power demand. Energy storage systems use these high-frequency fluctuations to determine when to charge or discharge, thus addressing instantaneous power fluctuations and ensuring grid frequency and voltage stability.

[0049] Low-frequency fluctuations are trends in power that change slowly over time, such as peak-valley variations in load within a day or seasonal load changes. Low-frequency trend decomposition can be obtained using wavelet decomposition, Fourier low-pass filtering, or sliding window trend analysis methods. The obtained low-frequency fluctuation component is used to analyze power changes over long periods, providing a basis for peak shaving and energy dispatch in energy storage.

[0050] Long-term energy variation refers to the total amount of energy that energy storage needs to store or release over a longer time scale. The daily load curve is the curve of the change of grid load over time in a day, reflecting peak and valley characteristics. Combining energy variation with the load curve forms the long-term power demand, which is the power curve that the energy storage system needs to provide over a long period, used for peak shaving, valley reduction and optimization of new energy consumption.

[0051] Decomposing historical power fluctuation data into short-term and long-term demand can clearly distinguish between rapidly fluctuating and slowly changing power characteristics in the power grid. This enables energy storage systems to respond quickly to instantaneous power fluctuations, ensuring grid frequency and voltage stability, while also rationally scheduling long-term energy to achieve peak-valley balance and optimize renewable energy consumption, thereby improving the safety, reliability, and economy of power grid operation.

[0052] S2. Calculate the quantiles of the short-term power demand and the long-term power demand, and use the quantiles as the initial upper limit of the energy storage charging and discharging power.

[0053] In this embodiment of the invention, quantiles represent the value of data at a certain percentage position after sorting. For example, the 50th percentile (median) means that half of the data is less than this value; the 90th percentile means that 90% of the data is less than this value. The initial upper limit of energy storage charging and discharging power refers to the maximum charging or discharging power allowed for the energy storage system during the initial planning stage.

[0054] Specifically, the step of calculating the quantiles of the short-term power demand and the long-term power demand, and using these quantiles as the initial upper limit of the energy storage charging and discharging power, includes:

[0055] The short-time power demand and the long-time power demand are respectively decomposed according to the symbol to obtain the short-time charge-discharge power sequence and the long-time charge-discharge power sequence;

[0056] The quantiles of the short-time charge-discharge power sequence and the long-time charge-discharge power sequence are calculated respectively to obtain the short-time charge-discharge power quantile and the long-time charge-discharge power quantile.

[0057] The upper limit of short-time charging power and the upper limit of short-time discharging power are generated based on the aforementioned short-time charging and discharging power quantiles;

[0058] The upper limit of long-term charging power and the upper limit of long-term discharging power are generated based on the long-term charging and discharging power quantiles.

[0059] The upper limit of short-time charging power, the upper limit of short-time discharging power, the upper limit of long-time charging power, and the upper limit of long-time discharging power are combined to form the initial upper limit value of energy storage charging and discharging power.

[0060] In detail, the power demand is separated according to positive and negative signs: positive values ​​represent discharge demand (energy storage supplies power to the grid), and negative values ​​represent charging demand (energy storage absorbs electrical energy from the grid). The resulting sequences correspond to the charging or discharging behavior of energy storage in short and long time, respectively.

[0061] For the split charging and discharging sequences, statistical distribution characteristics are analyzed, and typical power levels are represented by quantiles to obtain short-term and long-term charging / discharging power quantiles, reflecting the power demand intensity of energy storage at different time scales. The maximum allowable charging and discharging power of energy storage under short-term fluctuations is determined using the short-term charging and discharging quantiles. The maximum charging and discharging power of energy storage during long-term peak shaving or valley shaving is determined using the long-term charging and discharging quantiles. Combining these four upper limits yields the initial charging and discharging power upper limit of the energy storage system, simultaneously constraining both short-term and long-term operation.

[0062] By splitting short-term and long-term power demands according to their signs and calculating quantiles, the charging and discharging capabilities of energy storage systems at different time scales can be scientifically determined. This generates upper limits for short-term and long-term charging and discharging, which are then aggregated into initial upper limits for energy storage. This ensures that energy storage can respond quickly to instantaneous power fluctuations, maintain grid frequency and voltage stability, and also allows for reasonable long-term energy dispatch, achieving peak-valley balance and optimizing renewable energy consumption, thereby improving the safety, reliability, and economy of grid operation.

[0063] S3. Calculate the power fluctuation range of the historical power fluctuation data, and generate a disturbance range based on the power fluctuation range and the initial upper limit value.

[0064] In this embodiment of the invention, the power fluctuation range refers to the magnitude of the power fluctuation and is used to measure the strength of the power grid fluctuation. Specifically, the upper fluctuation range indicates the extent to which the power exceeds the average value, and the lower fluctuation range indicates the extent to which the power falls below the average value.

[0065] Disturbance range: refers to the variable range set in energy storage scheduling or power analysis to cope with power fluctuations in the power grid.

[0066] In detail, the step of calculating the power fluctuation range of the historical power fluctuation data and generating a disturbance range based on the power fluctuation range and the initial upper limit value includes:

[0067] Calculate the power change sequence based on the historical power fluctuation data;

[0068] Calculate the upper and lower fluctuation amplitudes based on the power change sequence;

[0069] The upper fluctuation amplitude and the lower fluctuation amplitude are averaged respectively to obtain the upper fluctuation average amplitude and the lower fluctuation average amplitude;

[0070] The lower limit of the disturbance range is obtained by subtracting the initial upper limit value from the lower average fluctuation amplitude.

[0071] Add the initial upper limit value to the average amplitude of the upper fluctuation to obtain the upper limit of the disturbance range;

[0072] A disturbance interval is generated based on the upper limit and the lower limit of the disturbance interval.

[0073] In detail, the power difference between adjacent time points is calculated by arranging historical power fluctuation data in chronological order to obtain a power change sequence, which reflects the magnitude and direction of power change at each time step. This is the basis for analyzing the amplitude of power fluctuations. The calculation formula is shown below:

[0074]

[0075] in, Represents a sequence of power changes. This represents historical power fluctuation data at time t. This represents the historical power fluctuation data at time t-1.

[0076] The power change series is divided into positive and negative values: positive values ​​represent the magnitude of power increase (upward fluctuation), and negative values ​​represent the magnitude of power decrease (downward fluctuation). By statistically analyzing the positive and negative power changes, the upward and downward fluctuation amplitudes of power are obtained, which are used to measure the strength of the fluctuation.

[0077] Averaging all upward fluctuations yields the upper fluctuation level; averaging all downward fluctuations yields the lower fluctuation level. Subtracting the average lower fluctuation amplitude from the initial upper limit of energy storage gives the minimum allowable decrease in energy storage power, i.e., the lower limit of the disturbance range. This lower limit ensures that the energy storage will not exceed its safe operating range when responding to power decreases. Adding the average upper fluctuation amplitude to the initial upper limit of energy storage gives the maximum allowable increase in energy storage power, i.e., the upper limit of the disturbance range. This upper limit ensures that the energy storage has sufficient responsiveness to power increases while not exceeding the equipment's safe power. Combining the upper and lower limits yields the complete disturbance range, indicating that the energy storage power can be flexibly adjusted within this range.

[0078] The disturbance range is generated based on the historical power fluctuation range and the initial upper limit of energy storage, thus providing a safe and flexible operating range for energy storage charging and discharging. This can fully utilize the energy storage capacity to cope with grid power fluctuations, while avoiding energy storage overload or waste caused by instantaneous fluctuations, thereby achieving efficient scheduling of the energy storage system and stable grid operation.

[0079] S4. Randomly perturb the initial upper limit value using the perturbation interval to obtain several candidate upper limit values.

[0080] In this embodiment of the invention, the initial upper limit value is set as the vertex of the triangular distribution, the perturbation interval is used as the distribution interval to construct the triangular distribution, and then random sampling is performed under the triangular distribution to generate several candidate upper limit values, so that the candidate upper limit values ​​are more concentrated near the initial upper limit value, while retaining the possibility at both ends of the interval.

[0081] Specifically, the step of randomly perturbing the initial upper limit value using the perturbation interval to obtain several candidate upper limit values ​​includes:

[0082] The initial upper limit value is used as the vertex of the triangle, and the disturbance interval is used as the triangular distribution interval;

[0083] Construct a triangular distribution based on the triangle vertices and the triangular distribution interval;

[0084] Based on the triangular distribution, the initial upper limit value is randomly sampled to obtain several candidate upper limit values.

[0085] In detail, the upper limit of the initial charging and discharging power of the energy storage system is set as the vertex of the triangular distribution, representing the most likely value. The lower and upper limits of the previously calculated disturbance range are used as the interval boundaries of the triangular distribution to form the minimum and maximum values ​​of the distribution. The most likely value and the possible extreme value range can be considered at the same time.

[0086] The triangular distribution is characterized by the highest probability near the vertices, gradually decreasing towards both ends. It is suitable for representing scenarios where "the most likely value is in the center, with a small probability at the edges." The calculation formula is shown below:

[0087]

[0088]

[0089]

[0090] in, Indicates a triangular distribution. This indicates the lower limit of the disturbance range. This indicates the upper limit of the disturbance range. Indicates the initial upper limit value. This indicates the average amplitude of the fluctuation. This represents the average amplitude of the fluctuation.

[0091] Random sampling is performed under the constructed triangular distribution to generate multiple candidate upper limit values. Most of these candidate upper limit values ​​are concentrated near the initial upper limit, while also covering the possibilities at both ends of the interval.

[0092] By using the disturbance interval to generate candidate upper limit values ​​through triangular distribution random disturbance of the initial upper limit value, most candidate values ​​can be concentrated near the initial upper limit, while taking into account the possibility of extreme values ​​at both ends of the interval. This maintains the stability of the upper limit of energy storage charging and discharging, while introducing a certain degree of randomness, providing diversified references for subsequent optimization and scheduling, and making the energy storage system more flexible and reliable in dealing with grid power fluctuations.

[0093] S5. Using the preset multi-element energy storage scheduling model and the candidate upper limit value, the short-term disturbance and long-term peak shaving of the power grid are simulated, and the power grid frequency deviation and the health degradation rate of the energy storage equipment are calculated according to the simulation process.

[0094] In this embodiment of the invention, the multi-element energy storage scheduling model refers to a pre-established model used to coordinate the operation of multiple types of energy storage (such as batteries, supercapacitors, pumped hydro storage, etc.). Candidate upper limit values ​​are multiple possible values ​​for the upper limit of energy storage charging and discharging, generated through a triangular distribution, used in the scheduling model to simulate the operating effect of the energy storage system under different constraints.

[0095] Short-term disturbances: Power fluctuations in the power grid on the order of seconds to minutes, including instantaneous load changes and fluctuations in renewable energy generation. Long-term peak shaving: Power regulation of the power grid on the order of hours to days and seasonal scales, used for peak shaving and valley filling, optimizing load distribution, and improving the utilization rate of renewable energy.

[0096] Grid frequency deviation: refers to the deviation between the actual grid frequency and the rated frequency (such as 50 Hz or 60 Hz). Energy storage device health degradation rate: refers to the rate at which the performance of an energy storage system (such as a battery) deteriorates during charging and discharging.

[0097] In detail, the operation simulation of short-term disturbances and long-term peak shaving of the power grid using a preset multi-element energy storage dispatch model and the candidate upper limit value includes:

[0098] Based on the historical power fluctuation data, short-term disturbance scenarios and long-term peak-shaving scenarios are constructed.

[0099] Using the short-time and long-time power limits from the candidate upper limits as power constraints, a multi-element energy storage scheduling model is configured to generate a constraint scheduling model corresponding to the scenario. The constraint scheduling model includes a short-time scheduling model and a long-time scheduling model. The specific steps are as follows:

[0100] Extract short-term power grid fluctuation data of the short-term disturbance scenario from the historical power fluctuation data;

[0101] Extract the short-time charging power candidate upper limit value and the short-time discharging power candidate upper limit value from the candidate upper limit values;

[0102] The short-time scheduling model is obtained by applying power constraints to the preset multi-element energy storage scheduling model using the short-time charging power candidate upper limit value and the short-time discharging power candidate upper limit value.

[0103] The short-time scheduling model is used to perform short-time disturbance simulation on the short-time power grid fluctuation data to generate the energy storage charging and discharging power response.

[0104] A frequency response curve is constructed based on the energy storage charging and discharging power response.

[0105] Extract the long-term load curve and new energy output curve of the long-term peak shaving scenario from the historical power fluctuation data;

[0106] Extract the long-term charging power candidate upper limit value and the long-term discharging power candidate upper limit value from the candidate upper limit values;

[0107] The long-term charging power candidate upper limit value and the long-term discharging power candidate upper limit value are used to apply power constraints to the preset multi-element energy storage scheduling model to obtain the long-term scheduling model;

[0108] The long-term scheduling model is used to perform long-term peak shaving simulation on the long-term load curve and the new energy output curve to generate energy storage charging and discharging power curve and battery state of charge curve.

[0109] In detail, the calculation formula for the pre-defined multi-element energy storage dispatch model is as follows:

[0110]

[0111] in, This represents the state of charge of the i-th type of energy storage device at time t+1. This represents the state of charge of the i-th type of energy storage device at time t. Let represent the charging efficiency of the i-th type of energy storage device. This represents the charging power of the i-th type of energy storage device at time t. Indicates the length of the time step. This represents the discharge efficiency of the i-th type of energy storage device. This represents the discharge power of the i-th type of energy storage device at time t. This indicates the rated energy capacity of the energy storage device.

[0112] Using historical power fluctuation data, power changes in power grid operation are divided into short-term disturbances (rapid fluctuations at the second to minute level) and long-term peak shaving (load and renewable energy output changes at the hour to day level) according to time scale. Specific power fluctuation data are extracted from the short-term disturbance scenario to reflect the rapid power changes of the power grid in a short period of time, which is used for short-term dispatch model simulation.

[0113] From the candidate upper limit set, charging and discharging upper limits specifically for short-term power regulation are selected to provide constraints for the short-term scheduling model. The selected short-term charging and discharging upper limits are added to the multi-element energy storage scheduling model to form a scheduling model for short-term disturbances, ensuring that the model will not exceed the safe power limit of the energy storage device during operation.

[0114] The model inputs short-term disturbance data such as load fluctuations and frequency deviations of the power grid on a second or minute scale. Based on the dispatch strategy and the operating characteristics of the energy storage system, the model calculates the charging or discharging actions that the energy storage device should take under these disturbances, and generates a dynamic response curve of the energy storage charging and discharging power changing over time, which is used to evaluate the role of energy storage in grid frequency stability.

[0115] Time series data on load demand and renewable energy generation output are obtained from long-term peak-shaving scenarios for long-term energy storage scheduling simulation. Charging and discharging upper limits specifically for long-term peak-shaving are selected from a set of candidate upper limits to provide power constraints for the long-term scheduling model. These long-term charging and discharging upper limit constraints are then incorporated into the scheduling model to form a long-term scheduling model that addresses long-term load and renewable energy output variations.

[0116] Using the load curves of the power grid on an hourly or daily scale and the output curves of new energy sources as input data, the model combines the capacity limitations, charging and discharging efficiency, and operational constraints of the energy storage system to optimize the charging and discharging strategies of energy storage at different times to balance the differences between load and new energy output. The model outputs the charging and discharging power curves of energy storage over the entire long period and the corresponding battery state-of-charge curves, thereby comprehensively reflecting the energy regulation role and operating status of energy storage in long-term peak shaving.

[0117] By introducing candidate upper limits into the multi-element energy storage scheduling model and conducting short-term disturbance simulation and long-term peak-shaving simulation respectively, the role of energy storage in suppressing rapid fluctuations and regulating energy over long periods can be evaluated simultaneously. This generates frequency response curves, charge and discharge power curves, and battery state of charge curves, which can comprehensively reflect the operating characteristics of the energy storage system at different time scales. This ensures grid frequency stability, improves the absorption capacity of new energy sources, and takes into account the operational safety and lifespan management of energy storage devices.

[0118] In detail, the calculation of grid frequency deviation and energy storage device health degradation rate based on the simulation process includes:

[0119] Calculate the maximum frequency deviation and mean square error based on the frequency response curve.

[0120] The maximum frequency deviation and the mean square error are taken as the power grid frequency deviation;

[0121] Rainflow cycle counting is performed on the energy storage charge and discharge power curve and the battery state of charge curve to count the cycle depth, number of cycles and cycle rate of each charge and discharge cycle;

[0122] The health degradation rate of the energy storage device is calculated based on the cycle depth, the number of cycles, and the cycle rate using a preset energy storage device life model.

[0123] In detail, the fluctuation amplitude of the power grid during the regulation process is extracted from the frequency response curve, and the maximum frequency deviation (representing the maximum degree of frequency deviation from the rated value) and the mean square error (representing the average level of frequency deviation throughout the process) are calculated to quantify the frequency stability of the power grid. The calculation formula is shown below:

[0124]

[0125] in, Indicates the maximum frequency deviation. The frequency of the power grid represents the frequency response curve at time t. Indicates the rated frequency.

[0126]

[0127] in, Indicates mean square error. This represents the total simulation time in the frequency response curve. The frequency of the power grid represents the frequency response curve at time t. Indicates the rated frequency.

[0128] The two indicators are combined to form the frequency deviation evaluation result of the power grid in this simulation scenario, which is used to measure the stability of the power grid under short-term disturbances.

[0129] The rainflow cycle counting method is used to decompose the energy storage power and battery state-of-charge curves, extracting the depth (charge / discharge amplitude), number of cycles, and rate of each charge / discharge cycle to obtain the actual cycle status of the battery during operation. The cycle depth, number of cycles, and rate are input into a preset energy storage device lifespan model, and the health degradation rate of the energy storage device is calculated based on battery characteristics, quantifying the degree of performance degradation caused by this operation. The calculation formula is shown below:

[0130]

[0131] in, This indicates the health degradation rate of energy storage devices. This represents the lifespan model of energy storage devices.

[0132] The calculation formula for the preset energy storage device lifespan model is shown below:

[0133]

[0134] in, This represents the lifespan model of energy storage devices. This indicates capacity decay caused by cyclic aging. This indicates capacity decay caused by calendar aging.

[0135] By simultaneously calculating grid frequency deviation and energy storage device health degradation rate during the simulation process, the impact of operation on device lifespan can be quantified while evaluating the contribution of energy storage system to grid stability. This not only comprehensively reflects the effect of energy storage in short-term frequency regulation and long-term energy regulation, but also takes into account device health management, thereby achieving a balance between grid security, performance improvement and energy storage lifespan extension when optimizing scheduling strategies.

[0136] S6. Select a target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device, and use the target upper limit value of the energy storage charging and discharging power to ensure power supply and absorbance regulation for the coordinated operation of multiple energy storage systems.

[0137] In this embodiment of the invention, candidate upper limit values ​​are evaluated based on the simulated grid frequency deviation and the health degradation rate of energy storage devices. A target upper limit value is then selected from these values. The target upper limit value is used to coordinate the scheduling of the multi-element energy storage system, thereby achieving rapid response to short-term disturbances and energy regulation for long-term peak shaving. This ensures both the safe operation of the power grid and the optimization of new energy consumption efficiency.

[0138] Specifically, the step of selecting the target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device includes:

[0139] Based on the grid frequency deviation and the energy storage device health degradation rate, a two-dimensional scatter plot of all the candidate upper limit values ​​is drawn.

[0140] Generate a frequency deviation threshold vertical line based on a preset frequency deviation threshold;

[0141] Generate a health decay threshold level line based on the preset health decay threshold;

[0142] Fill the two-dimensional scatter plot with the vertical line of the frequency deviation threshold and the horizontal line of the health attenuation threshold to obtain the updated scatter plot;

[0143] Extract the candidate upper limit values ​​within the threshold intersection region of the updated scatter plot as feasible upper limit values;

[0144] Operational strategies for securing and regulating power supply;

[0145] When the operating strategy is a conservative strategy, the feasible upper limit value that is closest to the initial upper limit value and has the smallest health degradation rate of the energy storage device is taken as the target upper limit value.

[0146] When the operating strategy is an aggressive strategy, the maximum feasible upper limit value is used as the target upper limit value.

[0147] In detail, using grid frequency deviation and energy storage device health degradation rate as two coordinate axes, the results corresponding to each candidate upper limit value are plotted as a point to form a two-dimensional scatter plot, which intuitively shows the performance differences of different candidate upper limit values.

[0148] A vertical line is drawn along the frequency deviation direction in the scatter plot, representing the acceptable upper limit of the grid frequency deviation, used to screen candidate values ​​that meet frequency stability requirements. A horizontal line is drawn along the health degradation direction in the scatter plot, representing the acceptable upper limit of the health degradation of the energy storage device, used to screen candidate values ​​that meet lifetime management requirements. The vertical line of the frequency deviation threshold and the horizontal line of the health degradation threshold are added to the scatter plot, and their intersection area is marked, thereby updating the scatter plot and forming an intuitive feasible solution space. Candidate upper limit values ​​that simultaneously meet the frequency deviation threshold and health degradation threshold conditions are selected as feasible solutions, ensuring both grid stability and extended energy storage lifetime.

[0149] Determine the operating strategy to be adopted under different application scenarios, such as a conservative strategy (focusing on equipment protection) or an aggressive strategy (focusing on power regulation capabilities). If a conservative strategy is chosen, prioritize the candidate value from the feasible upper limit that is close to the initial upper limit and has the least impact on the lifespan of the energy storage equipment, ensuring robust operation and prioritizing lifespan. If an aggressive strategy is chosen, directly select the maximum value from the feasible upper limit to maximize the energy storage's ability to regulate the grid and absorb new energy sources.

[0150] To achieve a balance between frequency security and energy storage lifespan, a two-dimensional scatter plot and threshold screening are used to effectively narrow down the candidate range, ensuring that the selected target upper limit not only meets the grid's requirements for frequency stability but also avoids the energy storage device from decaying too quickly. At the same time, conservative and aggressive strategies are introduced, allowing the target upper limit to be flexibly adjusted according to different power security and consumption needs, thereby improving the reliability and adaptability of energy storage system regulation.

[0151] The target upper limit of energy storage charging and discharging power can be used as a constraint and scheduling benchmark in the coordinated operation of multiple energy storage systems: when the power grid experiences frequency fluctuations or voltage deviations, different types of energy storage units (such as batteries, supercapacitors, and flywheels) coordinate and share the regulation tasks according to the target upper limit, quickly respond to short-term disturbances and stabilize the operation of the power grid; at the same time, when the output of new energy sources is high and the load is low, the energy storage system absorbs surplus power in an orderly manner according to the target upper limit and releases it again during peak load periods, thereby improving the absorption level of new energy sources, ensuring the safety and stability of the power system, and achieving efficient energy utilization.

[0152] By introducing the quantiles of historical power fluctuation data as the initial upper limit of energy storage power and combining them with the power fluctuation amplitude to generate a disturbance range, the one-sidedness of setting a single empirical value can be avoided. By using random disturbances to form candidate upper limits, and cooperating with the multi-dimensional energy storage scheduling model to evaluate the simulated operation of short-term disturbances and long-term peak shaving with dual indicators of frequency deviation and health degradation, it is possible to ensure the safety and stability of the power grid while taking into account the lifespan of energy storage equipment. Finally, a target upper limit value that takes into account both the quality of power grid operation and the economic efficiency of equipment is selected, thereby achieving a more scientific and efficient power security and consumption regulation of the multi-dimensional energy storage system.

[0153] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0154] This invention acquires historical power fluctuation data of an energy storage system during grid operation. Based on this data, it generates short-term and long-term power demands, clearly distinguishing between rapidly fluctuating and slowly changing power characteristics in the grid. This allows the energy storage system to respond quickly to instantaneous power fluctuations, ensuring grid frequency and voltage stability, while also rationally scheduling long-cycle energy to achieve peak-valley balance and optimize renewable energy consumption. The invention also calculates the quantiles of the short-term and long-term power demands and uses these quantiles as initial upper limits for energy storage charging and discharging power. This scientifically determines the charging and discharging capabilities of the energy storage system at different time scales, generating short-term and long-term charging and discharging upper limits, which are then summarized as initial upper limits. Furthermore, the invention calculates the power fluctuation amplitude of the historical power fluctuation data and generates a disturbance range based on this amplitude and the initial upper limit, providing a safe and flexible operating range for energy storage charging and discharging. Finally, the invention uses this disturbance range to randomly perturb the initial upper limit, obtaining several candidate values. Choosing an upper limit value concentrates most candidate values ​​near the initial upper limit while also considering the possibility of extreme values ​​at both ends of the interval. This maintains the stability of the energy storage charging and discharging upper limit while introducing a certain degree of randomness. Using a preset multi-element energy storage scheduling model and the candidate upper limit values, the system simulates short-term disturbances and long-term peak shaving of the power grid. Based on the simulation process, the grid frequency deviation and the health degradation rate of the energy storage equipment are calculated. This allows for the evaluation of the energy storage system's contribution to grid stability while quantifying the impact of operation on equipment lifespan. It not only comprehensively reflects the effect of energy storage in short-term frequency regulation and long-term energy regulation but also takes into account equipment health management. Based on the grid frequency deviation and the health degradation rate of the energy storage equipment, a target upper limit value is selected from the candidate upper limit values. The target upper limit value of the energy storage charging and discharging power is then used to regulate the power supply and absorption of multi-element energy storage in a coordinated manner. This effectively finds a reasonable upper limit for the energy storage charging and discharging power, improves the grid regulation capability, alleviates energy storage lifespan degradation, and effectively balances power supply security and new energy absorption.

[0155] like Figure 2 The diagram shown is a functional block diagram of a multi-energy storage collaborative power guarantee and consumption regulation device provided in an embodiment of the present invention.

[0156] This disclosure provides a power supply guarantee and absorption regulation device with multi-energy storage coordination, which corresponds one-to-one with the power supply guarantee and absorption optimization regulation method with multi-energy storage coordination operation described in the above embodiments. Figure 2As shown, this multi-energy storage collaborative power guarantee and absorption regulation device 100 can be installed in electronic equipment. According to its functions, the multi-energy storage collaborative power guarantee and absorption regulation device 100 includes a power demand generation module 101, an initial upper limit value generation module 102, a disturbance range generation module 103, a candidate upper limit value generation module 104, a candidate upper limit value simulation module 105, and a target upper limit value application module 106. Detailed descriptions of each functional module are as follows:

[0157] The power demand generation module 101 is used to acquire historical power fluctuation data of the energy storage system during grid operation, and generate short-term power demand and long-term power demand based on the historical power fluctuation data.

[0158] The initial upper limit generation module 102 is used to calculate the quantiles of the short-term power demand and the long-term power demand, and use the quantiles as the initial upper limit of the energy storage charging and discharging power.

[0159] The disturbance interval generation module 103 is used to calculate the power fluctuation amplitude of the historical power fluctuation data, and generate a disturbance interval based on the power fluctuation amplitude and the initial upper limit value.

[0160] The candidate upper limit value generation module 104 is used to randomly perturb the initial upper limit value using the perturbation interval to obtain a number of candidate upper limit values.

[0161] The candidate upper limit simulation module 105 is used to simulate the operation of the power grid under short-term disturbances and long-term peak shaving using a preset multi-element energy storage scheduling model and the candidate upper limit value, and to calculate the power grid frequency deviation and the health degradation rate of the energy storage device based on the simulation process.

[0162] The target upper limit application module 106 is used to select a target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device, and to use the target upper limit value of the energy storage charging and discharging power to ensure power supply and absorption regulation for the coordinated operation of multiple energy storage systems.

[0163] In one embodiment, the power demand generation module 101 generates short-term and long-term power demands based on the historical power fluctuation data, for the following purposes:

[0164] Standard power fluctuation data is obtained by uniformizing the time step of the historical power fluctuation data;

[0165] The standard power fluctuation data is subjected to short-time filtering to obtain filtered power fluctuation data.

[0166] Extract the high-frequency fluctuation component of the filtered power fluctuation data;

[0167] Short-term power demand is generated based on the high-frequency fluctuation component;

[0168] The standard power fluctuation data is decomposed into low-frequency trends to obtain the low-frequency fluctuation component;

[0169] Long-term energy variation and daily load curves are generated based on the low-frequency fluctuation portion;

[0170] The long-term energy variation and the daily load curve are summarized into long-term power demand.

[0171] In one embodiment, the initial upper limit generation module 102 performs statistical analysis on the quantiles of the short-term power demand and the long-term power demand, and uses the quantiles as the initial upper limit of the energy storage charging and discharging power, for the following purposes:

[0172] The short-time power demand and the long-time power demand are respectively decomposed according to the symbol to obtain the short-time charge-discharge power sequence and the long-time charge-discharge power sequence;

[0173] The quantiles of the short-time charge-discharge power sequence and the long-time charge-discharge power sequence are calculated respectively to obtain the short-time charge-discharge power quantile and the long-time charge-discharge power quantile.

[0174] The upper limit of short-time charging power and the upper limit of short-time discharging power are generated based on the aforementioned short-time charging and discharging power quantiles;

[0175] The upper limit of long-term charging power and the upper limit of long-term discharging power are generated based on the long-term charging and discharging power quantiles.

[0176] The upper limit of short-time charging power, the upper limit of short-time discharging power, the upper limit of long-time charging power, and the upper limit of long-time discharging power are combined to form the initial upper limit value of energy storage charging and discharging power.

[0177] In one embodiment, the disturbance interval generation module 103 calculates the power fluctuation amplitude of the historical power fluctuation data and generates a disturbance interval based on the power fluctuation amplitude and the initial upper limit value, for the purpose of:

[0178] Calculate the power change sequence based on the historical power fluctuation data;

[0179] Calculate the upper and lower fluctuation amplitudes based on the power change sequence;

[0180] The upper fluctuation amplitude and the lower fluctuation amplitude are averaged respectively to obtain the upper fluctuation average amplitude and the lower fluctuation average amplitude;

[0181] The lower limit of the disturbance range is obtained by subtracting the initial upper limit value from the lower average fluctuation amplitude.

[0182] Add the initial upper limit value to the average amplitude of the upper fluctuation to obtain the upper limit of the disturbance range;

[0183] A disturbance interval is generated based on the upper limit and the lower limit of the disturbance interval.

[0184] In one embodiment, the candidate upper limit value generation module 104 performs random perturbation on the initial upper limit value using the perturbation interval to obtain several candidate upper limit values, for the purpose of:

[0185] The initial upper limit value is used as the vertex of the triangle, and the disturbance interval is used as the triangular distribution interval;

[0186] Construct a triangular distribution based on the triangle vertices and the triangular distribution interval;

[0187] Based on the triangular distribution, the initial upper limit value is randomly sampled to obtain several candidate upper limit values.

[0188] In one embodiment, the candidate upper limit simulation module 105 performs operational simulations of short-term disturbances and long-term peak shaving of the power grid using a preset multi-element energy storage scheduling model and the candidate upper limit values, for the following purposes:

[0189] Based on the historical power fluctuation data, short-term disturbance scenarios and long-term peak-shaving scenarios are constructed.

[0190] Extract short-term power grid fluctuation data of the short-term disturbance scenario from the historical power fluctuation data;

[0191] Extract the short-time charging power candidate upper limit value and the short-time discharging power candidate upper limit value from the candidate upper limit values;

[0192] The short-time scheduling model is obtained by applying power constraints to the preset multi-element energy storage scheduling model using the short-time charging power candidate upper limit value and the short-time discharging power candidate upper limit value.

[0193] The short-time scheduling model is used to perform short-time disturbance simulation on the short-time power grid fluctuation data to generate the energy storage charging and discharging power response.

[0194] A frequency response curve is constructed based on the energy storage charging and discharging power response.

[0195] Extract the long-term load curve and new energy output curve of the long-term peak shaving scenario from the historical power fluctuation data;

[0196] Extract the long-term charging power candidate upper limit value and the long-term discharging power candidate upper limit value from the candidate upper limit values;

[0197] The long-term charging power candidate upper limit value and the long-term discharging power candidate upper limit value are used to apply power constraints to the preset multi-element energy storage scheduling model to obtain the long-term scheduling model;

[0198] The long-term scheduling model is used to perform long-term peak shaving simulation on the long-term load curve and the new energy output curve to generate energy storage charging and discharging power curve and battery state of charge curve.

[0199] In one embodiment, the candidate upper limit simulation module 105, while performing calculations of grid frequency deviation and energy storage device health degradation rate based on the simulation process, is used for:

[0200] Calculate the maximum frequency deviation and mean square error based on the frequency response curve.

[0201] The maximum frequency deviation and the mean square error are taken as the power grid frequency deviation;

[0202] Rainflow cycle counting is performed on the energy storage charge and discharge power curve and the battery state of charge curve to count the cycle depth, number of cycles and cycle rate of each charge and discharge cycle;

[0203] The health degradation rate of the energy storage device is calculated based on the cycle depth, the number of cycles, and the cycle rate using a preset energy storage device life model.

[0204] In one embodiment, the target upper limit application module 106, when performing the task of filtering a target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the energy storage device health degradation rate, is used to:

[0205] Based on the grid frequency deviation and the energy storage device health degradation rate, a two-dimensional scatter plot of all the candidate upper limit values ​​is drawn.

[0206] Generate a frequency deviation threshold vertical line based on a preset frequency deviation threshold;

[0207] Generate a health decay threshold level line based on the preset health decay threshold;

[0208] Fill the two-dimensional scatter plot with the vertical line of the frequency deviation threshold and the horizontal line of the health attenuation threshold to obtain the updated scatter plot;

[0209] Extract the candidate upper limit values ​​within the threshold intersection region of the updated scatter plot as feasible upper limit values;

[0210] Operational strategies for securing and regulating power supply;

[0211] When the operating strategy is a conservative strategy, the feasible upper limit value that is closest to the initial upper limit value and has the smallest health degradation rate of the energy storage device is taken as the target upper limit value.

[0212] When the operating strategy is an aggressive strategy, the maximum feasible upper limit value is used as the target upper limit value.

[0213] In this invention, the specific limitations regarding a power supply guarantee and absorption regulation device with multi-energy storage synergy can be found in the above-described limitations regarding a power supply guarantee and absorption optimization regulation method with multi-energy storage synergy, and will not be repeated here. Each module in the aforementioned power supply guarantee and absorption regulation device with multi-energy storage synergy can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0214] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0215] Acquire historical power fluctuation data of the energy storage system during grid operation, and generate short-term and long-term power demand based on the historical power fluctuation data;

[0216] The quantiles of the short-term power demand and the long-term power demand are calculated, and these quantiles are used as the initial upper limit of the energy storage charging and discharging power.

[0217] Calculate the power fluctuation range of the historical power fluctuation data, and generate a disturbance range based on the power fluctuation range and the initial upper limit value;

[0218] The initial upper limit value is randomly perturbed using the perturbation interval to obtain several candidate upper limit values;

[0219] The short-term disturbance and long-term peak shaving of the power grid are simulated using a preset multi-element energy storage scheduling model and the candidate upper limit value. The power grid frequency deviation and the health degradation rate of the energy storage equipment are calculated based on the simulation process.

[0220] Based on the grid frequency deviation and the health degradation rate of the energy storage device, a target upper limit value is selected from the candidate upper limit values, and the target upper limit value of the energy storage charging and discharging power is used to ensure power supply and regulate the absorption of multiple energy storage systems in coordinated operation.

[0221] In the several embodiments provided by this invention, it should be understood that the disclosed devices and apparatuses can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0222] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0223] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0224] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0225] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0226] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0227] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using dedicated hardware-based apparatus that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0228] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0229] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for power supply security and optimized regulation through the coordinated operation of multiple energy storage systems, characterized in that, The method includes: Obtain the initial upper limit value of energy storage charging and discharging power; Calculate the power fluctuation range of historical power fluctuation data, and generate a disturbance range based on the power fluctuation range; The initial upper limit value is randomly perturbed using a perturbation interval to obtain multiple candidate upper limit values. This includes: constructing a probability distribution model centered on the initial upper limit value of the energy storage charging and discharging power, based on a preset perturbation interval. The probability distribution model is a triangular distribution with the initial upper limit value as the vertex and the perturbation interval as the distribution interval; and generating several candidate upper limit values ​​through random sampling using the probability distribution model. Based on the candidate upper limit values, the operation simulation of short-term disturbances and long-term peak shaving of the power grid is carried out to calculate the power grid frequency deviation and the health degradation rate of energy storage equipment. The target upper limit value is selected from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of energy storage equipment. The target upper limit value is then used to ensure power supply and regulate the consumption of multi-energy storage systems in a coordinated manner.

2. The power supply guarantee and consumption optimization regulation method for multi-energy storage synergistic operation as described in claim 1, characterized in that, The initial upper limit value for obtaining the energy storage charging and discharging power includes: Acquire historical power fluctuation data during power grid operation; Short-term and long-term power demand are generated based on historical power fluctuation data. The quantiles of short-term and long-term power demand are calculated, and these quantiles are used as the initial upper limit of energy storage charging and discharging power.

3. The power supply guarantee and consumption optimization regulation method for multi-energy storage synergistic operation as described in claim 2, characterized in that, The process of generating short-term and long-term power demand based on historical power fluctuation data includes: Historical power fluctuation data is standardized to obtain standard power fluctuation data. The standard power fluctuation data is subjected to frequency domain separation processing to obtain short-term power demand characterizing high-frequency fluctuations and long-term power demand characterizing low-frequency trends. The frequency domain separation process includes: extracting the high-frequency fluctuation component through filtering technology to generate short-term power demand, and extracting the low-frequency fluctuation component through trend decomposition technology to generate long-term power demand.

4. The method for power supply guarantee and optimized regulation of multi-energy storage system with coordinated operation as described in claim 3, characterized in that, The specific steps for obtaining the initial upper limit value include: The short-time power demand and the long-time power demand are respectively divided into short-time charge-discharge power sequences and long-time charge-discharge power sequences according to the sign of the power value; Calculate the quantiles of the short-time charge-discharge power sequence and the long-time charge-discharge power sequence, respectively; Based on the quantiles, the upper limits of charging and discharging power for short and long periods are determined; By combining the various power limits, an initial upper limit value for the energy storage charging and discharging power is formed.

5. The power supply guarantee and consumption optimization regulation method for the coordinated operation of multiple energy storage systems as described in claim 1, characterized in that, The calculation of historical power fluctuation data includes the amplitude of power fluctuations, and the generation of disturbance intervals based on the amplitude of power fluctuations, including: Based on the power change statistical characteristics of historical power fluctuation data, the upper and lower fluctuation amplitudes of power are calculated. The power change statistical characteristics include the average fluctuation amplitude calculated based on the power change sequence. Based on the initial upper limit of the energy storage charging and discharging power, and combined with the upper and lower fluctuation amplitudes, the upper and lower limits of the disturbance range are determined. The disturbance interval is generated based on the upper and lower limits of the disturbance interval.

6. The power supply guarantee and consumption optimization regulation method for the coordinated operation of multiple energy storage systems as described in claim 1, characterized in that, The operational simulation of short-term disturbances and long-term peak shaving of the power grid based on candidate upper limits includes: Based on historical power fluctuation data, short-term disturbance simulation scenario and long-term peak shaving simulation scenario are constructed respectively. The short-time and long-time power limits in the candidate upper limit values ​​are used as power constraints, and a multi-dimensional energy storage scheduling model is configured to generate a constraint scheduling model corresponding to the scenario. The constrained scheduling model is used to simulate the operation of the corresponding scenario, and the simulation results are output to evaluate the grid frequency characteristics and the health status of energy storage devices.

7. The power supply guarantee and consumption optimization regulation method for the coordinated operation of multiple energy storage systems as described in claim 6, characterized in that, The calculation of grid frequency deviation and energy storage device health degradation rate includes: The extraction of quantitative indicators of power grid frequency stability is based on the frequency response curve. The extraction of quantitative indicators of power grid frequency stability includes calculating the maximum frequency deviation and the mean square error of the frequency deviation. Based on the energy storage charge and discharge power curves and state of charge curves, the health degradation rate of energy storage devices is calculated through cyclic stress analysis. The quantitative indicators of grid frequency stability and the health degradation rate of energy storage devices are used together as the basis for evaluating the performance of the upper limit of energy storage power.

8. The method for power supply guarantee and optimized regulation of multi-energy storage system with coordinated operation as described in claim 1, characterized in that, The process of selecting the target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device includes: A two-dimensional evaluation space is constructed based on the grid frequency deviation and energy storage device health degradation rate corresponding to each candidate upper limit value; In the two-dimensional evaluation space, a frequency deviation threshold and a health decay rate threshold are set, and feasible upper limits that meet the threshold constraints are selected. Based on the preset operating strategy, the final target upper limit value is selected from the feasible upper limit values.

9. A device for power supply guarantee and optimized regulation through the coordinated operation of multiple energy storage systems, characterized in that, The device includes: The initial upper limit generation module is used to obtain the initial upper limit value of the energy storage charging and discharging power; The disturbance range generation module is used to calculate the power fluctuation amplitude of historical power fluctuation data and generate a disturbance range based on the power fluctuation amplitude. The candidate upper limit value generation module is used to randomly perturb the initial upper limit value using a perturbation interval to obtain multiple candidate upper limit values, including: constructing a probability distribution model based on a preset perturbation interval with the initial upper limit value of the energy storage charging and discharging power as the center, wherein the probability distribution model is a triangular distribution with the initial upper limit value as the vertex and the perturbation interval as the distribution interval; and using the probability distribution model to perform random sampling to generate several candidate upper limit values. The candidate upper limit simulation module is used to simulate the operation of the power grid under short-term disturbances and long-term peak shaving based on the candidate upper limit values, and to calculate the power grid frequency deviation and the health degradation rate of energy storage devices. The target upper limit application module is used to select the target upper limit value from the candidate upper limit values ​​based on the grid frequency deviation and the health degradation rate of the energy storage device, and to use the target upper limit value to ensure power supply and consumption regulation for the coordinated operation of multiple energy storage systems.

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