Energy storage frequency modulation method and device based on power transaction and storage medium

Abstract

The invention relates to the field of electricity transaction, and discloses an energy storage frequency modulation method and device based on electricity transaction, and a storage medium. The method comprises the following steps: calculating the volatility of a contract price sequence to obtain a price volatility, and calculating the output volatility of energy storage actual output power to obtain an output volatility; performing performance allocation on the performance guarantee capacity to obtain a performance scheduling instruction, and performing intra-day prediction compensation on the day rolling capacity to obtain a prediction compensation instruction; based on the price fluctuation rate and the output fluctuation rate, carrying out output proportion distribution on the frequency modulation capacity, and generating a frequency modulation bidding instruction; and generating energy storage control data based on the performance scheduling instruction, the predictive compensation instruction and the predictive compensation instruction, and performing frequency modulation output control on the energy storage capacity according to the energy storage control data. According to the embodiment of the invention, frequency modulation is effectively participated while the performance of the transaction contract is guaranteed, energy storage attenuation is delayed, and the energy storage life is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power transaction, in particular to a power transaction-based energy storage frequency modulation method, device and storage medium. BACKGROUND

[0002] With the promotion of the "double carbon" goal, the proportion of new energy power generation represented by wind power and photovoltaic power is rapidly increasing. The intermittency and volatility of new energy output have brought great challenges to the stability of the power grid frequency. Energy storage systems, with their fast response speed and high regulation accuracy, have become the core resources to support the stable operation of high-proportion new energy power grids and provide frequency modulation auxiliary services. At the same time, the establishment and improvement of the power spot market and the auxiliary service market have created the possibility of "energy arbitrage" and "capacity reserve" for energy storage systems in addition to single frequency modulation services. Energy storage operation is transforming from providing single technical services to participating in complex power markets and pursuing comprehensive business models with full life cycle economics.

[0003] Large-scale energy storage power stations of million kilowatt-hour level and above, especially those energy storage projects matched with "new energy bases", have become a development trend. Such power stations often use hybrid energy storage architectures (such as power supercapacitors, energy lithium batteries, and long-time energy storage technology combinations) to balance the response requirements of different time scales. Their operation faces more complex multi-objective optimization problems: they not only need to meet the fulfillment requirements of multi-period power transaction contracts such as medium and long-term and day-ahead contracts bundled with new energy power generation, but also need to flexibly respond to real-time frequency modulation instructions of the power grid, and need to consider the technical characteristics and life attenuation of different energy storage media. However, the existing technical solutions are difficult to fully release the value of large-scale energy storage when adapting to such large-scale, hybrid, and multi-application scenario energy storage power stations, as the decision-making model only relies on power market price signals to separate the operation correlation of energy storage power stations and matched new energy stations, and thus there is a need for a technical solution to the current technical problem. SUMMARY

[0004] The main purpose of the present application is to solve the technical problem that the decision-making model only relies on power market price signals to separate the operation correlation of energy storage power stations and matched new energy stations.

[0005] The first aspect of the present application provides a power transaction-based energy storage frequency modulation method, the capacity of the energy storage including: fulfillment guarantee capacity, daily rolling capacity, frequency modulation capacity, the steps of the power transaction-based energy storage frequency modulation method including: acquiring a contract price sequence of a power transaction platform and an actual output power of the energy storage; calculating the volatility of the contract price sequence to obtain a price volatility rate, and calculating the volatility of the actual output power of the energy storage to obtain an output volatility rate; According to the preset daily average performance allocation algorithm, the performance guarantee capacity is allocated to obtain performance scheduling instructions, and according to the preset fluctuation compensation algorithm, the daily rolling capacity is predicted and compensated intraday to obtain prediction compensation instructions. When the price volatility is not greater than a preset price threshold and the output volatility is not greater than a preset output threshold, the frequency modulation capacity is allocated according to a preset fixed ratio to generate a frequency modulation bidding instruction. When the price volatility is not greater than a preset price threshold and the output volatility is greater than a preset output threshold, the frequency modulation capacity is subjected to a smoothing and locking deduction process according to a preset smoothing ratio to obtain an unlocked frequency modulation capacity. The unlocked frequency modulation capacity is then allocated according to a preset fixed ratio to generate a frequency modulation bidding instruction. When the price volatility is greater than the preset price threshold, the frequency modulation capacity is deducted in an emergency manner according to the preset emergency ratio to obtain the trading frequency modulation capacity, and the trading frequency modulation capacity is allocated in an output ratio according to the preset fixed ratio to generate a frequency modulation bidding instruction. Based on the performance scheduling instruction, the performance scheduling instruction, and the prediction compensation instruction, energy storage control data is generated, and frequency regulation output control is performed on the energy storage capacity according to the energy storage control data.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the step of allocating the performance guarantee capacity according to a preset daily average performance allocation algorithm to obtain performance scheduling instructions includes: C1=max(α*Q) long C reserve ); Where C1 is the performance guarantee capacity allocated by the performance scheduling instruction, α is the safety factor, and Q long C is the average daily volume of electricity fulfilled over a preset period. reserve To allocate reserve capacity.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the step of performing intraday prediction compensation on the daily rolling capacity according to a preset fluctuation compensation algorithm to obtain a prediction compensation instruction includes: C2=Q day -C1+△C; △C=P forecast *β; Where C2 is the daily rolling capacity allocated by the predicted compensation command, and Q day For daily rolling statistics of transaction volume, C1 is the performance guarantee capacity allocated by the performance dispatch instruction, △C is the fluctuation compensation capacity, and P forecast β represents the instantaneous predicted output power, and β is the fluctuation coefficient.

[0008] Optionally, in a third implementation form of the first aspect of the present application, the step of calculating the volatility of the contract price sequence to obtain a price volatility comprises: σ = | (P cur -P avg ) / P avg |; wherein σ is the price volatility, P cur is the average price of the spot market in the previous N minutes, and P avg is the average price of the spot market at the same time in the previous N-2 trading days, wherein N is a positive integer greater than 3.

[0009] Optionally, in a fourth implementation form of the first aspect of the present application, the step of calculating the output volatility of the actual output power of the energy storage to obtain an output volatility comprises: λ = | (P act -P forecast ) / P forecast |; wherein λ is the output volatility, P act is the actual output power of the energy storage, and P forecast is the instantaneous predicted output power.

[0010] Optionally, in a fifth implementation form of the first aspect of the present application, the energy storage comprises a molten salt energy storage, a battery energy storage, and a capacitor energy storage, wherein the capacity of the molten salt energy storage, the capacity of the capacitor energy storage, and the capacity of the battery energy storage are all set to respond to the rolling capacity on the day, the guarantee capacity for performance, and the frequency modulation capacity.

[0011] Optionally, in a sixth implementation form of the first aspect of the present application, after the step of performing frequency modulation output control on the capacity of the energy storage according to the energy storage control data, the method further comprises: reading the SOH value and the temperature value of the battery energy storage; when the SOH value is not greater than a preset de-rating threshold or the temperature value is not less than a preset temperature threshold, performing de-rating processing on the battery energy storage.

[0012] Optionally, in a seventh implementation form of the first aspect of the present application, after the step of performing frequency modulation output control on the capacity of the energy storage according to the energy storage control data, the method further comprises: calculating a daily net income value according to a preset daily rolling income algorithm; performing division and adjustment of the rolling capacity on the day, the guarantee capacity for performance, and the frequency modulation capacity of the battery energy storage according to the daily net income value and a preset feedback analysis algorithm, to generate a new rolling capacity on the day, a new guarantee capacity for performance, and a new frequency modulation capacity.

[0013] The second aspect of the present application provides a power transaction-based energy storage frequency regulation device, comprising a memory and at least one processor, the memory has instructions stored therein, and the memory and the at least one processor are interconnected by a line; the at least one processor invokes the instructions in the memory to enable the power transaction-based energy storage frequency regulation device to perform the power transaction-based energy storage frequency regulation method described above.

[0014] The third aspect of the present application provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, cause the computer to perform the power transaction-based energy storage frequency regulation method described above.

[0015] In the embodiment of the present application, by collecting the contract price sequence of the power transaction platform and the actual output power of the energy storage, the contract price sequence and the actual output power of the energy storage are aligned in data source, the volatility of the contract price sequence and the output volatility of the actual output power of the energy storage are calculated, the size of the price volatility and the price threshold value and the size of the output volatility and the output threshold value are analyzed, the frequency regulation bidding instruction is calculated through the analysis result, the energy storage control data is obtained by combining the fulfillment scheduling instruction, the fulfillment scheduling instruction, and the predicted compensation instruction, the total capacity of the energy storage is divided into fulfillment guarantee capacity, daily rolling capacity, and frequency regulation capacity to match the output power volatility and the price volatility, the energy storage can effectively participate in frequency regulation while guaranteeing the fulfillment of the new energy supporting transaction contract, the energy storage attenuation is delayed and the energy storage life is improved, and the operation correlation of the power market price signal, the energy storage power station, and the supporting new energy field station is realized. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 FIG. 1 is a schematic diagram of one embodiment of the power transaction-based energy storage frequency regulation method in the embodiment of the present application; Figure 2 FIG. 2 is a schematic diagram of another specific embodiment after step 107 of the power transaction-based energy storage frequency regulation method in the embodiment of the present application; Figure 3 FIG. 3 is a schematic diagram of another specific embodiment after step 107 of the power transaction-based energy storage frequency regulation method in the embodiment of the present application; Figure 4 FIG. 4 is a schematic diagram of one embodiment of the power transaction-based energy storage frequency regulation device in the embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a power transaction-based energy storage frequency regulation method, device, and storage medium.

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided so as to more completely and thoroughly understand the present disclosure. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of protection of the present disclosure.

[0019] In the description of embodiments of the present disclosure, the term "comprising" and its conjugations should be understood as open-ended, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "an embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. can refer to different or the same objects. Other explicit and implicit definitions can also be included below.

[0020] For ease of understanding, the specific flow of the embodiments of the present disclosure is described below. Please refer to Figure 1 An embodiment of the energy storage frequency modulation method based on power transaction in the embodiments of the present disclosure is shown in FIG. 1. The capacity of the energy storage includes: contract guarantee capacity, daily rolling capacity, frequency modulation capacity. The steps of the energy storage frequency modulation method based on power transaction include: 101, collecting the contract price sequence of the power transaction platform and the actual output power of the energy storage; In this embodiment, the industrial-grade data acquisition gateway deployed on the power station side uses industrial standard protocols such as OPC UA and Modbus-TCP to obtain the real-time state of each energy storage medium, including the voltage and SOC of supercapacitors, the SOC and SOH of battery energy storage, the heat storage temperature and heat of long-time energy storage such as molten salt, the charging and discharging power and efficiency of each PCS, and the actual output power of the energy storage.

[0021] The daily decomposed power and price of the medium and long-term contract, the daily rolling transaction power and price, the real-time / predicted price sequence of the spot market, and other data are obtained from the power transaction platform to obtain the contract price sequence.

[0022] It should be noted that the scheme can also be connected to the grid dispatching mechanism to obtain AGC instruction signals, including demand power, direction, and timestamp, and obtain frequency modulation service settlement rules, such as mileage price and performance coefficient K value algorithm.

[0023] The scheme can also connect to the new energy monitoring platform to obtain the future 0-4 hour power prediction data and actual output data of the supporting wind farm / photovoltaic power station.

[0024] The time scale of the collected data was standardized by using linear interpolation or sampling to unify the time resolution of all data to 5 minutes. Then, the multi-source data with the standardized time scale were aligned based on a unified timestamp to form a unified time-series dataset required for subsequent analysis.

[0025] Specifically, there are at least three types of energy storage: molten salt energy storage, battery energy storage, and capacitor energy storage. Molten salt energy storage media has the characteristic of long-term energy storage. Its capacity is set as the performance guarantee capacity; if the performance guarantee capacity is insufficient to meet demand, it can be supplemented by battery and capacitor energy storage. Capacitor energy storage media has the characteristic of fast response; its capacity is mainly set as the frequency regulation capacity; if the frequency regulation capacity is insufficient to meet demand, it can be supplemented by battery and molten salt energy storage. Battery energy storage is more flexible, but its degradation rate is faster than molten salt and capacitor energy storage. Therefore, its capacity is set as the response compensation daily rolling capacity, performance guarantee capacity, and frequency regulation capacity, flexibly providing power for the output of all three capacities.

[0026] 102. Calculate the volatility of the contract price series to obtain the price volatility, and calculate the volatility of the actual output power of the energy storage to obtain the output volatility; In this embodiment, the volatility of the contract price sequence is calculated within a preset time interval to obtain the price volatility. Then, within a set statistical time interval, the volatility of the actual output power of the energy storage is calculated to obtain the output volatility.

[0027] Specifically, the step of calculating the volatility of the contract price series to obtain the price volatility includes: σ=|(P cur -P avg ) / P avg |; Where σ is the price volatility, P cur P represents the average spot price over the previous N minutes. avg It represents the average spot price at the same time in the previous N-2 trading days, where N is a positive integer greater than 3.

[0028] P cur It can be set to the average spot price for the current 5-minute period, P avg It can be set to the average of the spot prices at the same time over the past 3 trading days.

[0029] Specifically, the step of calculating the output volatility of the actual output power of the energy storage to obtain the output volatility includes: λ=|(P act -P forecast ) / P forecast |; Where λ is the output volatility, Pact P represents the actual output power of the energy storage. forecast This is for instantaneous prediction of output power.

[0030] 103. Based on the preset daily average performance allocation algorithm, the performance guarantee capacity is allocated to obtain the performance scheduling instruction; and based on the preset fluctuation compensation algorithm, the daily rolling capacity is predicted and compensated intraday to obtain the prediction compensation instruction. In this embodiment, based on the total available capacity C of the power station total Perform hierarchical dynamic allocation: The contract performance guarantee capacity C1 is a medium- to long-term capacity reserve, with the dual objectives of "ensuring contract performance" and "meeting grid reserve requirements." Calculation formula: C1=max(α*Q) long C reserve ); Where C1 is the performance guarantee capacity allocated by the performance scheduling instruction, α is the safety factor, and Q long C is the average daily volume of electricity fulfilled over a preset period. reserve To allocate reserve capacity, the safety factor is set within the range of 1.1-1.2. The capacity allocation module calls the medium characteristic database to query the technical and economic parameters of each medium. Following the principle of "long-term energy storage taking priority for baseload," the guaranteed capacity C1 is preferentially allocated to long-term energy storage media such as molten salt energy storage. Any shortfall is supplemented by lithium batteries. This allocation result is determined at the beginning of each day and remains essentially unchanged throughout the day.

[0031] The daily rolling capacity C2 is a daily rolling cycle capacity adjustment used to meet daily rolling transactions and cope with fluctuations in new energy sources. Calculation formula: C2=Q day -C1+△C; △C=P forecast *β; Where C2 is the daily rolling capacity allocated by the predicted compensation command, and Q day For daily rolling statistics of transaction volume, C1 is the performance guarantee capacity allocated by the performance dispatch instruction, △C is the fluctuation compensation capacity, and P forecast β represents the instantaneous predicted output power, and β is the fluctuation coefficient. The fluctuation coefficient can range from 0.05 to 0.1, and is set to 0 if the performance guarantee capacity C2 is negative. After the daily rolling trading closes, the system recalculates the fluctuation compensation capacity ΔC and the performance guarantee capacity C2 based on the latest ultra-short-term new energy forecasts, and allocates them mainly to lithium batteries with faster response times and some supercapacitors.

[0032] Frequency regulation capacity C3 represents the allocation of capacity between the spot market and the frequency regulation cycle, and is a key parameter for real-time optimization within the day. C3=C total-C1-C2. Based on the determination results of steps 104-106, the frequency modulation capacity C3 is allocated differentially.

[0033] 104. When the price volatility is not greater than a preset price threshold and the output volatility is not greater than a preset output threshold, the frequency modulation capacity is allocated according to a preset fixed ratio to generate a frequency modulation bidding instruction. In this embodiment, the price threshold δ can be set to 5%, and the output threshold γ can be set to 12%. When the price volatility σ is less than or equal to the price threshold δ and the output volatility λ is less than or equal to the output threshold γ, this state is considered a high-value frequency modulation window, and a hybrid medium tiered allocation strategy is adopted. A fixed proportion template is set through the capacity allocation module. Supercapacitor energy storage accounts for 15% of the frequency modulation capacity C3, used to respond to high-frequency commands ≤10s. Battery energy storage accounts for 70% of the frequency modulation capacity C3, used to respond to medium-frequency commands 10s-5min. Molten salt energy storage accounts for 15% of the frequency modulation capacity C3, used to provide continuous power support >5min or low-frequency regulation. The proportions can be adjusted according to the actual medium configuration to generate frequency modulation bidding commands.

[0034] 105. When the price volatility is not greater than the preset price threshold and the output volatility is greater than the preset output threshold, the frequency modulation capacity is subjected to a suppression locking deduction process according to the preset suppression ratio to obtain the unlocked frequency modulation capacity, and the unlocked frequency modulation capacity is allocated according to the preset fixed ratio to generate a frequency modulation bidding instruction. In this embodiment, when the price volatility σ is less than or equal to the price threshold δ and the output volatility λ is greater than the output threshold γ, the current state is considered to be the frequency modulation-smoothing coordination period. A portion of the frequency modulation capacity C3, such as 35%, is reserved specifically for smoothing new energy volatility. This portion of the capacity is marked as "smoothing dedicated" and does not participate in frequency modulation bidding. The remaining 65% of the frequency modulation capacity C3 is unlocked frequency modulation capacity, which is allocated to each medium according to a fixed ratio to participate in frequency modulation and generate frequency modulation bidding instructions.

[0035] 106. When the price volatility is greater than the preset price threshold, the frequency modulation capacity is deducted in an emergency manner according to the preset emergency ratio to obtain the trading frequency modulation capacity, and the trading frequency modulation capacity is allocated in an output ratio according to the preset fixed ratio to generate a frequency modulation bidding instruction. In this embodiment, when the price volatility σ is greater than the price threshold δ, this state is considered a trading priority period. The vast majority of the frequency regulation capacity C3, such as 90-95%, is used as trading frequency regulation capacity for charging and discharging in the spot market. Only 5-10% of the total supercapacitor capacity, representing 5% of the frequency regulation capacity C3, is reserved as emergency frequency regulation capacity, in a hot standby state. It only activates upon receiving an extremely high-priority emergency AGC command to ensure the bottom line of grid safety. The trading frequency regulation capacity is allocated according to a fixed ratio, generating frequency regulation bidding commands.

[0036] 107. Based on the performance scheduling instruction, the prediction compensation instruction, and the prediction compensation instruction, generate energy storage control data, and perform frequency modulation output control on the energy storage capacity according to the energy storage control data.

[0037] In this embodiment, the energy storage control data is generated by combining the medium- and long-term performance scheduling instructions, the daily rolling forecast compensation instructions, and the short-term transaction energy storage control data. The energy storage control data is an AGC instruction, and the power control is realized after the AGC instruction is transmitted to the energy storage system.

[0038] Specifically, the AGC command decomposition unit employs a variational mode decomposition algorithm to adaptively decompose the received raw AGC power command sequence into three intrinsic mode function components in the time-frequency domain: a high-frequency component (period < 10s), a mid-frequency component (period 10s-5min), and a low-frequency component (period > 5min). These three decomposed components are distributed in real time: the high-frequency component is sent to the supercapacitor's power conversion system controller, the mid-frequency component to the lithium battery pack's PCS controller, and the low-frequency component to the long-term energy storage (e.g., molten salt) PCS / thermal control system. Each controller then executes local closed-loop power control upon receiving the command.

[0039] Please see Figure 2 , Figure 2 This is a schematic diagram of a specific embodiment following step 107 of the energy storage frequency regulation method based on electricity trading in this invention. The specific implementation methods following step 107 are as follows: 1071. Read the SOH value and temperature value of the battery energy storage; 1072. When the SOH value is not greater than a preset derating threshold or the temperature value is not less than a preset temperature threshold, the battery energy storage is drated.

[0040] In steps 1071-1072, lithium battery life is improved through SOH constraints and thermal management. The SOH value of the lithium battery is monitored in real time, and a hard constraint is set in the control logic. When SOH ≤ 85%, a derating strategy is triggered, permanently reducing the maximum allowable frequency modulation output of that battery cell by 30%. This cell is recommended for priority use in performing relatively stable spot trading charge and discharge operations. Battery cluster temperature data is continuously read. When the temperature of any battery cluster ≥ 35℃, a derating command is sent to the PCS controller of that cluster, reducing its current output by 10-20% and increasing the cooling system power until the temperature returns to a safe range.

[0041] Existing technologies prioritize short-term gains at the expense of battery life. This invention uses key lifespan parameters such as State of Harmony (SOH) and operating temperature as hard constraints in the core control logic. Through proactive management methods such as dynamic derating and operating condition optimization, it significantly extends the lifespan of lithium batteries. While it may limit peak output at certain times in the short term, it drastically reduces the huge capital expenditures incurred due to premature battery replacement. Comprehensive calculations show a significant reduction in the total lifespan cost per unit of frequency regulation, resulting in a substantial increase in the total project lifespan revenue, achieving a balance between short-term gains and long-term asset preservation.

[0042] Please see Figure 3 , Figure 3 This is a schematic diagram of another specific embodiment following step 107 of the energy storage frequency regulation method based on electricity trading in this invention. The specific implementation methods following step 107 are as follows: 1073. Calculate the daily net profit value according to the preset daily rolling profit algorithm; 1074. Based on the daily net revenue value and the preset feedback analysis algorithm, the daily rolling capacity, performance guarantee capacity, and frequency regulation capacity of the battery energy storage are divided and adjusted to generate new daily rolling capacity, new performance guarantee capacity, and new frequency regulation capacity.

[0043] In steps 1073-1074, based on the SOH decay curve model of lithium batteries, the daily lifespan loss cost due to cycle and frequency regulation output is calculated, plus fixed operation and maintenance costs. Daily net revenue is then calculated. If the system detects insufficient available frequency regulation capacity during the "high-value frequency regulation window" due to improper capacity allocation, resulting in a loss of potential frequency regulation revenue, the system automatically reduces the declared electricity volume by 10-20% during the next daily rolling transaction declaration based on the analysis results. This released capacity will be converted into frequency regulation reserve capacity. If it is related to the deviation in new energy fluctuation prediction, the fluctuation coefficient β is automatically fine-tuned (e.g., increased from 0.08 to 0.09) to make the future fluctuation compensation capacity ΔC calculation more conservative, reserving more space for frequency regulation. After the above cyclical data feedback fine-tuning, a new daily rolling capacity, a new performance guarantee capacity, and a new frequency regulation capacity are obtained.

[0044] Furthermore, the ratio of the frequency modulation compensation unit price to the average spot price is calculated in real time, i.e., the profit coefficient K. A preset strategy is implemented in the lithium battery output management subunit: if K ≥ 2, the lithium battery is allowed to respond to the intermediate frequency command at 80-100% of its rated power; if 1.5 ≤ K < 2, the output is limited to 50-80%; if K < 1.5, the output priority is further reduced. This achieves economically driven flexible power control.

[0045] After each day's transactions, the system can automatically summarize the electricity volume and corresponding settlement amount for medium- and long-term, daily rolling, and spot transactions, as well as the frequency regulation service mileage and compensation amount, and can break down the contribution by medium. Monthly reports are generated for long-term re-evaluation, analyzing the relationship between the decline trend of lithium battery SOH and profitability, and optimizing parameters such as the threshold of safety factor α and profitability factor K to form a closed loop of continuous improvement.

[0046] Figure 4 This is a schematic diagram of the structure of an energy storage frequency regulation device 400 based on power trading, provided by an embodiment of the present invention. The energy storage frequency regulation device 400 based on power trading can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 410 and memory 420, and one or more storage media 430 storing application programs 433 or data 432. The memory 420 and storage media 430 can be temporary or persistent storage. The program stored in the storage media 430 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the energy storage frequency regulation device 400 based on power trading. Furthermore, the processor 410 may be configured to communicate with the storage media 430 and execute the series of instruction operations in the storage media 430 on the energy storage frequency regulation device 400 based on power trading.

[0047] The energy storage frequency regulation device 400 for power trading may also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input / output interfaces 460, and / or one or more operating systems 431, such as Windows Server, Mac OS X, Unix, Linux, Free BSD, etc. Those skilled in the art will understand that... Figure 4 The illustrated structure of an energy storage frequency regulation device based on electricity trading does not constitute a limitation on energy storage frequency regulation devices for electricity trading. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0048] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the energy storage frequency regulation method based on power trading.

[0049] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0050] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0051] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A frequency regulation method for energy storage based on electricity trading, characterized in that, The energy storage capacity includes: performance guarantee capacity, daily rolling capacity, and frequency regulation capacity. The steps of the energy storage frequency regulation method based on electricity trading include: Collect contract price sequences and actual output power of energy storage from the power trading platform; Calculate the volatility of the contract price series to obtain the price volatility, and calculate the volatility of the actual output power of the energy storage to obtain the output volatility; According to the preset daily average performance allocation algorithm, the performance guarantee capacity is allocated to obtain performance scheduling instructions, and according to the preset fluctuation compensation algorithm, the daily rolling capacity is predicted and compensated intraday to obtain prediction compensation instructions. When the price volatility is not greater than a preset price threshold and the output volatility is not greater than a preset output threshold, the frequency modulation capacity is allocated according to a preset fixed ratio to generate a frequency modulation bidding instruction. When the price volatility is not greater than a preset price threshold and the output volatility is greater than a preset output threshold, the frequency modulation capacity is subjected to a smoothing and locking deduction process according to a preset smoothing ratio to obtain an unlocked frequency modulation capacity. The unlocked frequency modulation capacity is then allocated according to a preset fixed ratio to generate a frequency modulation bidding instruction. When the price volatility is greater than the preset price threshold, the frequency modulation capacity is deducted in an emergency manner according to the preset emergency ratio to obtain the trading frequency modulation capacity, and the trading frequency modulation capacity is allocated in an output ratio according to the preset fixed ratio to generate a frequency modulation bidding instruction. Based on the performance scheduling instruction, the prediction compensation instruction, and the prediction compensation instruction, energy storage control data is generated, and the energy storage capacity is frequency-modulated and output controlled according to the energy storage control data.

2. The energy storage frequency regulation method based on electricity trading according to claim 1, characterized in that, The step of allocating the performance guarantee capacity according to a preset daily average performance allocation algorithm to obtain performance scheduling instructions includes: C1=max(α*Q long ,C reserve ); Where C1 is the performance guarantee capacity allocated by the performance scheduling instruction, α is the safety factor, and Q long C is the average daily volume of electricity fulfilled over a preset period. reserve To allocate reserve capacity.

3. The energy storage frequency regulation method based on electricity trading according to claim 2, characterized in that, The step of performing intraday prediction compensation on the daily rolling capacity according to a preset fluctuation compensation algorithm to obtain a prediction compensation instruction includes: C2=Q day -C1+△C; △C=P forecast *b; Where C2 is the daily rolling capacity allocated by the predicted compensation command, and Q day For daily rolling statistics of transaction volume, C1 is the performance guarantee capacity allocated by the performance dispatch instruction, △C is the fluctuation compensation capacity, and P forecast β represents the instantaneous predicted output power, and β is the fluctuation coefficient.

4. The energy storage frequency regulation method based on electricity trading according to claim 1, characterized in that, The step of calculating the volatility of the contract price series to obtain the price volatility includes: σ=|(P cur -P avg ) / P avg |; Where σ is the price volatility, P cur P represents the average spot price over the previous N minutes. avg It represents the average spot price at the same time in the previous N-2 trading days, where N is a positive integer greater than 3.

5. The energy storage frequency regulation method based on electricity trading according to claim 1, characterized in that, The step of calculating the output volatility of the actual output power of the energy storage to obtain the output volatility includes: λ=|(P act -P forecast ) / P forecast |; Where λ is the output volatility, P act P represents the actual output power of the energy storage. forecast This is for instantaneous prediction of output power.

6. The energy storage frequency regulation method based on electricity trading according to claim 1, characterized in that, The energy storage includes molten salt energy storage, battery energy storage, and capacitor energy storage. The capacity of the molten salt energy storage, the capacity of the capacitor energy storage, and the capacity of the battery energy storage are all set as daily rolling capacity for response compensation, performance guarantee capacity, and frequency regulation capacity, respectively.

7. The energy storage frequency regulation method based on electricity trading according to claim 6, characterized in that, After the step of performing frequency modulation output control on the energy storage capacity based on the energy storage control data, the method further includes: Read the SOH value and temperature value of the battery energy storage; When the SOH value is not greater than a preset derating threshold or the temperature value is not less than a preset temperature threshold, the battery energy storage is drated.

8. The energy storage frequency regulation method based on electricity trading according to claim 7, characterized in that, After the step of performing frequency modulation output control on the energy storage capacity based on the energy storage control data, the method further includes: The daily net profit is calculated based on the preset daily rolling profit algorithm; Based on the daily net revenue value and the preset feedback analysis algorithm, the daily rolling capacity, performance guarantee capacity, and frequency regulation capacity of the battery energy storage are divided and adjusted to generate new daily rolling capacity, new performance guarantee capacity, and new frequency regulation capacity.

9. A power trading-based energy storage frequency regulation device, characterized in that, The energy storage frequency regulation device based on power trading includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor invokes the instructions in the memory to cause the power trading-based energy storage frequency regulation device to execute the power trading-based energy storage frequency regulation method as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the energy storage frequency regulation method based on power trading as described in any one of claims 1-8.

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