An energy storage power joint bidding method, device, equipment and storage medium

Abstract

The application discloses an energy storage power joint bidding method, device, equipment and storage medium. The method comprises the following steps: according to a pre-prepared day-ahead joint bidding optimization model and multi-source market information and energy storage state information, an optimal power execution sequence is predicted with the maximum joint net income of each time period in a day as a target. The optimal power execution sequence is executed, and it is judged whether the remaining time period needs to be re-bid according to each preset trigger condition. In the case of determining that the re-bid is needed, the actual SOC of the current time period is taken as an initial state, the re-bid power execution sequence of the remaining time period set is re-predicted according to the re-joint bidding optimization model, and the power fine-tuning processing is performed on the energy storage equipment according to the re-bid power execution sequence, so that the market income, the execution feasibility and the battery life constraint can be better considered, the full-scene demand of the energy storage participating in the spot market can be adapted, and the market income, the execution feasibility and the operation safety can be improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization and control technology, and in particular to a method, apparatus, equipment and storage medium for energy storage power joint bidding. Background Technology

[0002] With the continuous advancement of the construction of new power systems, independent energy storage power stations and industrial and commercial energy storage systems are gradually participating in the electricity and ancillary services market as market players. Compared with traditional thermal power units, energy storage, with its bidirectional charging and discharging regulation, millisecond-level dynamic response, and flexible ramping capabilities, is naturally suited for scenarios such as energy arbitrage, frequency regulation and peak shaving, and backup services, becoming a key support for smoothing the fluctuations of new energy sources and ensuring the stability of the power grid.

[0003] However, existing energy storage pricing methods generally have significant limitations: First, they are disconnected from day-ahead and intraday market decisions, lacking a multi-timescale coordination mechanism, making it difficult for static plans to adapt to dynamic market price fluctuations; Second, their optimization objectives are singular, focusing only on short-term arbitrage gains without comprehensively considering multiple constraints such as SOC evolution, SOH decay, cycle life loss, deviation assessment penalties, and frequency regulation reserve capacity, which can easily lead to hidden losses where "revenue cannot cover battery depreciation"; Third, intraday pricing relies heavily on manual experience, lacking quantitative triggering logic based on SOC deviation, electricity price jumps, and shifts in frequency regulation demand, resulting in delayed response and strong subjectivity.

[0004] The aforementioned problems have led to a serious disconnect between the planned trajectory and physical execution of energy storage systems in actual operation. This makes it impossible to fully leverage their rapid adjustment advantages or maximize their benefits throughout the entire life cycle. Therefore, there is an urgent need for a joint bidding method that integrates the constraints of multiple markets and has dynamic response capabilities. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for joint bidding of energy storage power, which can better balance market returns, feasibility of execution, and battery life constraints, and adapt to the full-scenario needs of energy storage participating in the spot market, thereby improving market returns, feasibility of execution, and operational safety.

[0006] According to one aspect of the present invention, a method for joint bidding on energy storage power is provided. The method includes:

[0007] Acquire multi-source market information on energy storage power and energy storage status information of energy storage devices. The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price, and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number, and battery temperature.

[0008] Based on the pre-established day-ahead joint bidding optimization model and the multi-source market information and energy storage status information, with the goal of maximizing the joint net revenue in each period of the day, the optimal power execution sequence in each period of the day is predicted.

[0009] Execute the optimal power execution sequence, and receive the latest market price and execution feedback for each time period in a rolling window manner, and determine whether to re-quote for the remaining time period based on each preset trigger condition;

[0010] If it is determined that a re-quotation is required, the actual SOC of the current time period is taken as the initial state. Based on the re-joint quotation optimization model, the re-quotation power execution sequence of the remaining time period set is re-predicted, and the power fine-tuning of the energy storage device is performed according to the re-quotation power execution sequence.

[0011] After each day's operation, the daily revenue, costs, and execution deviations of the plan are statistically analyzed, and the statistical data is fed back for processing to achieve adaptive updates of the parameters of each optimization model.

[0012] According to another aspect of the present invention, an energy storage power joint bidding device is provided. The device includes:

[0013] The energy storage power information acquisition module is used to acquire multi-source market information about energy storage power and energy storage status information of energy storage devices. The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number and battery temperature.

[0014] The power execution sequence determination module is used to predict the optimal power execution sequence for each period of the day, based on a pre-defined day-ahead joint bidding optimization model, the multi-source market information, and the energy storage status information, with the goal of maximizing the joint net revenue for each period of the day.

[0015] The execution sequence requote module is used to execute the optimal power execution sequence, and receive the latest market price and execution feedback for each time period in a rolling window manner, and determine whether to requote for the remaining time period based on various preset trigger conditions;

[0016] The power fine-tuning execution module is used to, when it is determined that a re-bidding is required, take the actual SOC of the current time period as the initial state, re-predict the re-bidding power execution sequence of the remaining time period set according to the re-joint bidding optimization model, and perform power fine-tuning processing on the energy storage device according to the re-bidding power execution sequence.

[0017] The quotation execution plan optimization module is used to calculate the execution deviations of various revenues, costs, and plans after each day's operation, and to process the statistical data to achieve adaptive updates of the parameters of each optimization model.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] 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 energy storage power joint bidding method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the energy storage power joint bidding method according to any embodiment of the present invention.

[0023] The technical solution of this invention predicts the optimal power execution sequence for each time period of the day by using a pre-defined day-ahead joint bidding optimization model, multi-source market information, and energy storage status information, with the goal of maximizing the joint net revenue for each time period within the day. The optimal power execution sequence is executed, and the latest market price and execution feedback for each time period are received in a rolling window manner. Based on various preset trigger conditions, it is determined whether a re-bidding is needed for the remaining time periods. If a re-bidding is determined, the current actual SOC is used as the initial state, and the re-bidding optimization model is used to re-predict the re-bidding power execution sequence for the remaining time periods. Based on the re-bidding power execution sequence, the power of the energy storage device is fine-tuned. After the daily operation ends, the execution deviations of various revenues, costs, and plans within the day are statistically analyzed. The statistical data is fed back to achieve adaptive updates of the parameters of each optimization model. This solves the problem of the disconnect between day-ahead and intraday market decisions in existing energy storage bidding methods, achieving a balance between short-term revenue and long-term asset preservation. It better balances market revenue, execution feasibility, and battery life constraints, adapting to the full-scenario needs of energy storage participating in the spot market, thereby improving market revenue, execution feasibility, and operational safety.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0026] Figure 1 This is a flowchart of a joint bidding method for energy storage power provided by an embodiment of the present invention;

[0027] Figure 2 This is a structural diagram of an energy storage power joint bidding device provided according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the energy storage power joint bidding method of this invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a non-exclusive inclusion; for example, a process, method, system, product, or apparatus 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 apparatus.

[0031] Figure 1 This is a flowchart illustrating a method for joint bidding of energy storage power according to an embodiment of the present invention. This embodiment is applicable to the refined operation and management of energy storage systems. The method can be executed by an energy storage power joint bidding device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S101. Obtain multi-source market information on energy storage power and energy storage status information of energy storage devices.

[0033] The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price, and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number, and battery temperature.

[0034] Specifically, the energy storage management system establishes data interfaces with the power trading platform, ancillary service platform, dispatching system, and BMS / EMS to obtain day-ahead market electricity price forecasts, intraday rolling market electricity prices, frequency regulation capacity compensation prices, frequency regulation mileage compensation prices, market rule parameters, and energy storage status information such as current SOC, SOH, maximum charge and discharge power, equivalent cycle count, and battery temperature.

[0035] To facilitate unified modeling, the input information for time period t is represented as the following state vector:

[0036] ;

[0037] in, This indicates the current market electricity price forecast. This indicates the market update of electricity prices for the day. This indicates the price for frequency modulation capacity compensation. This indicates the mileage compensation price for FM tuning. Indicates the state of charge. Indicates health status. These represent the maximum available charging power and discharging power, respectively. This indicates a frequency modulation (FM) demand signal.

[0038] S102. Based on the pre-established day-ahead joint bidding optimization model and the multi-source market information and energy storage status information, with the goal of maximizing the joint net revenue for each period of the day, predict the optimal power execution sequence for each period of the day.

[0039] The division of time periods within a day can be determined according to the actual situation. The preferred time granularity for quoting in this invention is 15 minutes, so the day is divided into 96 time periods.

[0040] For each time period, the multi-source market information and the energy storage status information are substituted into the day-ahead joint bidding optimization model. With the goal of maximizing the joint net revenue for each time period within the day, the optimal power execution sequence for each time period within the day can be determined.

[0041] For example, the day-ahead joint pricing optimization model is as follows:

[0042] ;

[0043] in, This refers to the total net income in the current period. This refers to the market forecast electricity price for the period t days prior. This refers to the market-quoted electricity volume before time period t. This refers to the frequency modulation capacity compensation price for time period t. This refers to the frequency regulation capacity quotation quantity during time period t. This refers to the time period t frequency modulation mileage compensation price, This refers to the revenue corresponding to the frequency modulation mileage in time period t. This refers to the cost of lifespan loss over a period of time t. This refers to the cost of assessing deviations during the time period t.

[0044] For example, the method for determining the lifetime loss cost is as follows:

[0045] ;

[0046] in, This refers to the cost of lifespan depletion, This refers to the standard single-cycle loss cost. This refers to the depth of charge / discharge correction function. It refers to the equivalent cyclic increment.

[0047] To avoid the pricing model solely pursuing short-term gains, this invention directly incorporates lifetime attrition cost into the optimization objective. The standard single-cycle attrition cost is defined as follows:

[0048] ;

[0049] in, This refers to the total investment cost of the battery system. This refers to the rated standard cycle life.

[0050] Considering the impact of different depths of charge / discharge (DOD) on lifetime consumption, the DOD correction function is as follows:

[0051] ;

[0052] in, and For empirical parameters This represents the depth of charge / discharge corresponding to time period t.

[0053] To improve adaptability to electricity price forecast errors, the day-ahead electricity price forecast can be represented as an interval [λ̲_t, λ̄_t]. The above optimization model can be solved under the most unfavorable price scenario to obtain a robust day-ahead pricing scheme.

[0054] S103. Execute the optimal power execution sequence, and receive the latest market price and execution feedback for each time period in a rolling window manner, and determine whether it is necessary to re-quote for the remaining time period based on each preset trigger condition.

[0055] During the daytime operation phase, energy storage power is controlled according to the optimal power execution sequence. Simultaneously, the latest market price and execution feedback for each time period are received via a rolling window, and a determination is made based on various preset trigger conditions to determine whether a retendering is needed for the remaining time period. If any preset trigger condition is met, a retendering is determined; if none of the trigger conditions are met, a retendering is determined not to be needed for the remaining time period.

[0056] For example, the preset triggering conditions include, but are not limited to:

[0057] ;

[0058] ;

[0059] The FM demand signal undergoes a directional change;

[0060] in, This refers to the actual SOC value. This refers to predicting the SOC value. This refers to the SOC deviation threshold. This refers to the intraday rolling electricity price. This refers to the day-ahead electricity price forecast. This refers to the electricity price deviation threshold.

[0061] In other words, the preset triggering conditions include SOC deviation triggering conditions, electricity price deviation triggering conditions, and frequency regulation demand signal directional change triggering conditions.

[0062] For example, considering the impact of SOH on effective energy capacity, the SOC state update equation at each stage is set as follows:

[0063] ;

[0064] in, and These are charging efficiency and discharging efficiency, respectively. and These represent the charging power and discharging power, respectively, with Δt being the time interval length. This represents the rated energy capacity. This formula reflects that the lower the SOH (State of Energy), the smaller the effective capacity, and the more significant the change in SOC (State of Charge) under the same power output.

[0065] The following constraints will be further imposed in the current phase:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] Where SOC represents the state of charge at time t, Indicates the charging power during time period t. Represents the discharge power during time period t. These represent the maximum available charging power and discharging power, respectively. Indicates charging status flag bit, Indicates charging status flag bit, Indicates the SOC at the end of the day. Indicates the ideal initial value, Indicates the allowable threshold for end-point deviation. Indicates the quoted quantity of frequency modulation capacity for time period t. Indicates the minimum SOC threshold for frequency modulation. This indicates the highest SOC threshold for frequency modulation.

[0072] The above constraints correspond to SOC operating range constraints, charging and discharging power constraints, charging and discharging mutual exclusion constraints, first-end balance constraints, and frequency regulation reserve capacity constraints, respectively.

[0073] S104. If it is determined that a re-quotation is required, take the actual SOC of the current time period as the initial state, and re-predict the re-quotation power execution sequence of the remaining time period set according to the re-quotation optimization model. Then, perform power fine-tuning on the energy storage device according to the re-quotation power execution sequence.

[0074] Specifically, when any triggering condition is met, the actual SOC of the current time period is used as the initial state, and the remaining time period set is used as the optimization interval to resolve the re-reported power execution sequence. After obtaining the re-reported power execution sequence, the energy storage device is subjected to power fine-tuning processing.

[0075] For example, the re-joint quotation optimization model is as follows:

[0076] ;

[0077] in, This refers to the total net income during the remaining part of the daytime period. This refers to the rolling forecast of electricity prices within the k-day period. This refers to the adjusted quoted electricity volume within the time period k days. This refers to the time period k-frequency modulation capacity compensation price. This refers to the price quotation for k-frequency modulation capacity during the specified time period. This refers to the lifespan loss cost during time period k. This refers to the cost of assessing deviations in time period k. This refers to the disturbance penalty coefficient, This refers to the predicted electricity volume for the k-day period.

[0078] For example, the step of performing power fine-tuning processing on the energy storage device according to the re-reported power execution sequence includes: generating power adjustment information corresponding to each time moment according to the re-reported power execution sequence; and performing power fine-tuning processing on the energy storage device within the SOC safety range and rated power limit range when there is a deviation between the power adjustment information and the current quotation execution plan.

[0079] For example, the SOC safety range and rated power limit range are determined as follows: ;in, This refers to power adjustment information, This refers to the fine-tuning ratio coefficient. This refers to the rated power of energy storage.

[0080] In other words, after determining the power adjustment information for each moment, it is determined whether the power adjustment information exceeds the upper limit of the safe range. If it does not exceed the limit, power fine-tuning is performed according to the power adjustment information; if it exceeds the limit, fine-tuning is performed according to the upper limit of the safe range.

[0081] S105. After the daily operation ends, calculate the daily revenue, costs, and execution deviations of the plan, and feed the statistical data back to process and achieve adaptive updates of the parameters of each optimization model.

[0082] At the end of each day's operation, the system automatically generates a daily operation report, which includes statistics on energy market revenue, ancillary service revenue, lifetime depreciation costs, deviation assessment costs, and planned / executed deviations. Total net revenue can be expressed as:

[0083] ;

[0084] in, This refers to energy market revenue, This refers to revenue from ancillary services, This refers to the cost of lifespan depletion, This refers to the cost of deviation assessment.

[0085] The system further analyzes the electricity price prediction error and execution deviation, and feeds the statistical results back to the price prediction module, the life loss model, and the threshold parameter module for model updates in the next trading cycle.

[0086] The technical solution of this invention predicts the optimal power execution sequence for each time period of the day by using a pre-defined day-ahead joint bidding optimization model, multi-source market information, and energy storage status information, with the goal of maximizing the joint net revenue for each time period within the day. The optimal power execution sequence is executed, and the latest market price and execution feedback for each time period are received in a rolling window manner. Based on various preset trigger conditions, it is determined whether a re-bidding is needed for the remaining time periods. If a re-bidding is determined, the current actual SOC is used as the initial state, and the re-bidding optimization model is used to re-predict the re-bidding power execution sequence for the remaining time periods. Based on the re-bidding power execution sequence, the power of the energy storage device is fine-tuned. After the daily operation ends, the execution deviations of various revenues, costs, and plans within the day are statistically analyzed. The statistical data is fed back to achieve adaptive updates of the parameters of each optimization model. This solves the problem of the disconnect between day-ahead and intraday market decisions in existing energy storage bidding methods, achieving a balance between short-term revenue and long-term asset preservation. It better balances market revenue, execution feasibility, and battery life constraints, adapting to the full-scenario needs of energy storage participating in the spot market, thereby improving market revenue, execution feasibility, and operational safety.

[0087] This embodiment uses a scenario of a standalone energy storage power station with a rated power of 50MW and a rated capacity of 100MWh participating in a provincial electricity market transaction to specifically illustrate the method of the present invention. This power station can simultaneously participate in the day-ahead energy market, intraday market, and frequency regulation ancillary service market. The bidding time granularity is set to 15 minutes, thus the next day is divided into 96 time periods.

[0088] (I) Multi-source market and energy storage status information acquisition

[0089] The energy storage management system obtains the clearing price forecast for each time period of the next day in the day-ahead market and its 95% confidence interval upper and lower bounds through the data interface with the power trading platform; obtains the intraday market rolling updated price information; obtains the frequency regulation demand signal, frequency regulation mileage compensation price and frequency regulation capacity compensation price in the frequency regulation ancillary service market; and analyzes market rule parameters, including the minimum bidding unit, the bidding time deadline and deviation assessment rules.

[0090] Energy storage operation status data is obtained through the BMS: the current SOC value is set to operate within the range of 10% to 90%, the maximum available charging power and discharging power are both 50MW, the current SOH value is set to 95%, and the accumulated equivalent cycle count and average battery pack temperature for the day are uploaded synchronously. If the system status is as follows at the start of a certain rolling window... =0.62、 If the value is 0.95, then the corresponding state vector can be constructed according to the state vector of time period t and sent to the optimizer.

[0091] (II) Battery Cycle Life Loss Cost Modeling

[0092] This embodiment uses a lithium iron phosphate battery, and the relationship between its cycle life and depth of charge / discharge can be calibrated according to the life curve provided by the manufacturer. Let the total investment cost of the battery system be... The value is 150 million yuan, and the rated standard cycle life is... If the number of cycles is 6000, then according to the standard single-cycle loss cost formula, the standard single-cycle loss cost is 25000 yuan / cycle.

[0093] For the charging and discharging process of non-standard DODs, this embodiment takes empirical parameters a=1.0 and b=2.0. Then, according to the charging and discharging depth correction function formula, we have: .when When =0.50, the correction factor is 0.25; if the equivalent cyclic increment corresponding to this period is... =0.08, then according to the lifetime loss cost formula, the lifetime loss cost for this period is 500 yuan. This shows that the pricing model can achieve a quantitative balance between a high-yield but deep-cycle strategy and a low-loss but shallow-cycle strategy.

[0094] (III) Optimization of Joint Bidding in the Previous Stage

[0095] To maximize the combined net profit over 96 time periods the following day, a day-ahead joint bidding optimization model is constructed. The specific constraints are as follows: State of Charge (SOC) operating range constraint is set to 10% to 90% according to the formula; charging and discharging power constraint is set to ensure that the charging and discharging power in each time period does not exceed 50MW according to the formula; charging and discharging mutual exclusion constraint is set to ensure that charging and discharging cannot occur simultaneously in the same time period according to the formula; beginning and end balance constraint is set to ensure that the SOC returns to the 50% ± 5% range at the end of the day according to the formula; frequency regulation reserve capacity constraint is set to ensure that the SOC remains between 30% and 70% when participating in frequency regulation according to the formula.

[0096] In this embodiment, the current SOH value is 0.95, so the effective capacity can be approximated as 95MWh. If the discharge power within a certain 15-minute period... =20MW, discharge efficiency =0.96, Initial =0.62, then according to the SOC state update equation, the SOC decrease during this period can be calculated to be approximately 20 × 0.25 ÷ (0.96 × 100 × 0.95) = 0.0548, meaning the SOC at the end of the period is approximately 0.5652. Introducing SOH directly into the state update helps to make the pricing plan closer to the actual available capacity.

[0097] To address the uncertainty in electricity price forecasting, this embodiment uses the day-ahead electricity price forecast range output by the price forecasting model to solve for robust pricing solutions within the range based on the most unfavorable price scenario. For example, when the central value of the electricity price forecast for a certain period is 420 yuan / MWh, the lower bound is 380 yuan / MWh, and the upper bound is 460 yuan / MWh, the optimizer prioritizes pricing strategies that still have positive revenue and feasibility under the lower bound scenario. The solution process uses a mixed-integer linear programming solver and ensures completion before the day-ahead pricing deadline.

[0098] (iv) Intraday rolling requote correction

[0099] During the intraday operation phase, a rolling window cycle is executed every 15 minutes to trigger a requote decision. A SOC deviation threshold is set. =5%; Set the electricity price deviation threshold. =15%; simultaneously monitor whether the direction of the frequency modulation demand signal changes.

[0100] For example, in the 40th time period, the planned SOC for the day-ahead period is 0.48, and the actual SOC is 0.42. Therefore, |0.42 - 0.48| = 0.06, which is greater than 5%, satisfying the SOC deviation trigger condition. If the predicted day-ahead electricity price for this period is 420 yuan / MWh, and the intraday rolling electricity price is updated to 500 yuan / MWh, then the electricity price deviation ratio is approximately 19.05%, also greater than 15%, further satisfying the electricity price deviation trigger condition. When any of the above conditions are met, the system uses the current actual SOC as the initial state, the remaining time period as the optimization cycle, re-solves the joint bidding optimization model, and submits the revised bidding scheme through the trading platform interface.

[0101] If none of the three triggering conditions are met, the system will maintain the current day-to-day pricing scheme and directly proceed to the next scrolling window, thereby avoiding unnecessary frequent price changes and scheduling disturbances.

[0102] (v) Real-time fine-tuning and execution

[0103] During real-time operation, the energy storage system performs power tracking based on AGC (Automatic Guided Collection) commands issued by the dispatch center. When there is a deviation between the AGC command and the intraday revised pricing scheme, power fine-tuning is performed within the SOC (State of Charge) safety range. The real-time fine-tuning range is set at 10% of the rated power, i.e., ξ=0.10. If the rated power of the energy storage is 50MW, the allowable real-time fine-tuning limit is 5MW.

[0104] For example, if the planned discharge power for a certain period is 18MW, and the AGC requests an additional 2.5MW of regulation, the actual executed power will be 20.5MW, and the increment will not exceed the 5MW upper limit, so execution is permitted. If the AGC requests an additional 7MW of regulation, the system will trim the command according to the rated power and SOC boundary limits to avoid out-of-bounds operation.

[0105] (vi) Output of quotation results and execution feedback

[0106] After each day's operation, the system automatically summarizes and outputs the daily operation report, including: comparison of the day-ahead bid volume and the intraday revised bid volume for each time period, comparison of planned charging and discharging power and actual charging and discharging power, the SOC trajectory for the whole day, the settlement value of energy market revenue, the settlement value of frequency regulation market revenue, the equivalent cycle number, the statistics of life loss cost, and the statistics of electricity price prediction error.

[0107] For example, if the daily revenue from the energy market is 386,000 yuan, the revenue from the frequency regulation market is 94,000 yuan, the lifespan loss cost is 21,000 yuan, and the deviation assessment cost is 8,000 yuan, then the net revenue for that day is 451,000 yuan. The system further transmits the price error and execution deviation back to the electricity price prediction model and threshold parameter module to achieve continuous model correction for the next trading cycle.

[0108] Figure 2 This is a schematic diagram of a power storage joint bidding device provided in an embodiment of the present invention. Figure 2 As shown, the device includes:

[0109] The energy storage power information acquisition module 301 is used to acquire multi-source market information about energy storage power and energy storage status information of energy storage devices. The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number and battery temperature.

[0110] The power execution sequence determination module 302 is used to predict the optimal power execution sequence for each period of the day based on a pre-established day-ahead joint bidding optimization model, the multi-source market information, and the energy storage status information, with the goal of maximizing the joint net revenue for each period of the day.

[0111] The execution sequence requote module 303 is used to execute the optimal power execution sequence, and to receive the latest market price and execution feedback for each time period in a scrolling window manner, and to determine whether it is necessary to requote for the remaining time period based on various preset trigger conditions;

[0112] The power fine-tuning execution module 304 is used to, when it is determined that a re-bidding is required, take the actual SOC of the current time period as the initial state, re-predict the re-bidding power execution sequence of the remaining time period set according to the re-joint bidding optimization model, and perform power fine-tuning processing on the energy storage device according to the re-bidding power execution sequence.

[0113] The quotation execution plan optimization module 305 is used to calculate the execution deviation of various revenues, costs and plans during the day after the daily operation ends, and to process the statistical data to achieve adaptive updates of the parameters of each optimization model.

[0114] The technical solution of this invention predicts the optimal power execution sequence for each time period of the day by using a pre-defined day-ahead joint bidding optimization model, multi-source market information, and energy storage status information, with the goal of maximizing the joint net revenue for each time period within the day. The optimal power execution sequence is executed, and the latest market price and execution feedback for each time period are received in a rolling window manner. Based on various preset trigger conditions, it is determined whether a re-bidding is needed for the remaining time periods. If a re-bidding is determined, the current actual SOC is used as the initial state, and the re-bidding optimization model is used to re-predict the re-bidding power execution sequence for the remaining time periods. Based on the re-bidding power execution sequence, the power of the energy storage device is fine-tuned. After the daily operation ends, the execution deviations of various revenues, costs, and plans within the day are statistically analyzed. The statistical data is fed back to achieve adaptive updates of the parameters of each optimization model. This solves the problem of the disconnect between day-ahead and intraday market decisions in existing energy storage bidding methods, achieving a balance between short-term revenue and long-term asset preservation. It better balances market revenue, execution feasibility, and battery life constraints, adapting to the full-scenario needs of energy storage participating in the spot market, thereby improving market revenue, execution feasibility, and operational safety.

[0115] Optionally, the day-ahead joint pricing optimization model is as follows:

[0116] ;

[0117] in, This refers to the total net income in the current period. This refers to the market forecast electricity price for the period t days prior. This refers to the market-quoted electricity volume before time period t. This refers to the frequency modulation capacity compensation price for time period t. This refers to the frequency regulation capacity quotation quantity during time period t. This refers to the time period t frequency modulation mileage compensation price, This refers to the revenue corresponding to the frequency regulation mileage in time period t. This refers to the cost of lifespan loss over a period of time t. This refers to the cost of assessing deviations during the time period t.

[0118] Optionally, the method for determining the lifetime loss cost is as follows:

[0119] ;

[0120] in, This refers to the cost of lifespan depletion, This refers to the standard single-cycle loss cost. This refers to the depth of charge / discharge correction function. It refers to the equivalent cyclic increment.

[0121] Optionally, the preset triggering conditions include, but are not limited to:

[0122] ;

[0123] ;

[0124] The FM demand signal undergoes a directional change;

[0125] in, This refers to the actual SOC value. This refers to predicting the SOC value. This refers to the SOC deviation threshold. This refers to the intraday rolling electricity price. This refers to the day-ahead electricity price forecast. This refers to the electricity price deviation threshold.

[0126] Optionally, the re-joint quotation optimization model is as follows:

[0127] ;

[0128] in, This refers to the total net income during the remaining daytime period. This refers to the rolling forecast of electricity prices within the k-day period. This refers to the adjusted quoted electricity volume within the time period k days. This refers to the time period k-frequency modulation capacity compensation price. This refers to the price quotation for k-frequency modulation capacity during a given time period. This refers to the lifespan loss cost during time period k. This refers to the cost of assessing deviations in time period k. This refers to the disturbance penalty coefficient, This refers to the predicted electricity volume for the k-day period.

[0129] Optionally, the power fine-tuning execution module 304 is also specifically used for:

[0130] Based on the rereported power execution sequence, power adjustment information corresponding to each time point is generated;

[0131] When there is a deviation between the power adjustment information and the current quotation execution plan, the energy storage device is subjected to power fine-tuning within the SOC safety range and the rated power limit.

[0132] Optionally, the SOC safety range and rated power limit range are determined as follows:

[0133] ;

[0134] in, This refers to power adjustment information, This refers to the fine-tuning ratio coefficient. This refers to the rated power of energy storage.

[0135] The energy storage power joint bidding device provided in the embodiments of the present invention can execute the energy storage power joint bidding method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0136] Figure 3 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0137] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0138] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the energy storage power joint bidding method.

[0140] In some embodiments, the energy storage power co-bidding method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the energy storage power co-bidding method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the energy storage power co-bidding method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0144] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0146] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0147] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0148] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for joint bidding of energy storage power, characterized in that, include: Acquire multi-source market information on energy storage power and energy storage status information of energy storage devices. The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price, and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number, and battery temperature. Based on the pre-established day-ahead joint bidding optimization model and the multi-source market information and energy storage status information, with the goal of maximizing the joint net revenue in each period of the day, the optimal power execution sequence in each period of the day is predicted. Execute the optimal power execution sequence, and receive the latest market price and execution feedback for each time period in a rolling window manner, and determine whether to re-quote for the remaining time period based on each preset trigger condition; If it is determined that a re-quotation is required, the actual SOC of the current time period is taken as the initial state. Based on the re-joint quotation optimization model, the re-quotation power execution sequence of the remaining time period set is re-predicted, and the power fine-tuning of the energy storage device is performed according to the re-quotation power execution sequence. After each day's operation, the daily revenue, costs, and execution deviations of the plan are statistically analyzed, and the statistical data is fed back for processing to achieve adaptive updates of the parameters of each optimization model.

2. The method according to claim 1, characterized in that, The day-ahead joint pricing optimization model is as follows: ； in, This refers to the total net income in the current period. This refers to the market forecast electricity price for the period t days prior. This refers to the market-quoted electricity volume before time period t. This refers to the frequency modulation capacity compensation price for time period t. This refers to the frequency regulation capacity quotation quantity during time period t. This refers to the time period t frequency modulation mileage compensation price, This refers to the revenue corresponding to the frequency modulation mileage in time period t. This refers to the cost of lifespan loss over a period of time t. This refers to the cost of assessing deviations during the time period t.

3. The method according to claim 2, characterized in that, The method for determining the lifetime loss cost is as follows: ； in, This refers to the cost of lifespan depletion, This refers to the standard single-cycle loss cost. This refers to the depth of charge / discharge correction function. It refers to the equivalent cyclic increment.

4. The method according to claim 1, characterized in that, The preset triggering conditions include, but are not limited to: ； ； The FM demand signal undergoes a directional change; in, This refers to the actual SOC value. This refers to predicting the SOC value. This refers to the SOC deviation threshold. This refers to the intraday rolling electricity price. This refers to the day-ahead electricity price forecast. This refers to the electricity price deviation threshold.

5. The method according to claim 1, characterized in that, The re-joint pricing optimization model is as follows: ； in, This refers to the total net income during the remaining daytime period. This refers to the rolling forecast of electricity prices within the k-day period. This refers to the adjusted quoted electricity volume within the time period k days. This refers to the time period k-frequency modulation capacity compensation price. This refers to the price quotation for k-frequency modulation capacity during a given time period. This refers to the lifespan loss cost during time period k. This refers to the cost of assessing deviations in time period k. This refers to the disturbance penalty coefficient, This refers to the predicted electricity volume for the k-day period.

6. The method according to claim 1, characterized in that, The step of performing power fine-tuning processing on the energy storage device according to the rereported power execution sequence includes: Based on the rereported power execution sequence, power adjustment information corresponding to each time point is generated; When there is a deviation between the power adjustment information and the current quotation execution plan, the energy storage device is subjected to power fine-tuning within the SOC safety range and the rated power limit.

7. The method according to claim 1, characterized in that, The determination methods for the SOC safety range and rated power limit range are as follows: ； in, This refers to power adjustment information, This refers to the fine-tuning ratio coefficient. This refers to the rated power of energy storage.

8. A power storage joint bidding device, characterized in that, include: The energy storage power information acquisition module is used to acquire multi-source market information about energy storage power and energy storage status information of energy storage devices. The multi-source market information includes, but is not limited to, day-ahead market electricity price forecast, intraday rolling market electricity price, frequency regulation capacity compensation price, frequency regulation mileage compensation price and market rule parameters. The energy storage status information includes, but is not limited to, current SOC, current SOH, maximum charge and discharge power, equivalent cycle number and battery temperature. The power execution sequence determination module is used to predict the optimal power execution sequence for each period of the day, based on a pre-defined day-ahead joint bidding optimization model, the multi-source market information, and the energy storage status information, with the goal of maximizing the joint net revenue for each period of the day. The execution sequence requote module is used to execute the optimal power execution sequence, and receive the latest market price and execution feedback for each time period in a rolling window manner, and determine whether to requote for the remaining time period based on various preset trigger conditions; The power fine-tuning execution module is used to, when it is determined that a re-bidding is required, take the actual SOC of the current time period as the initial state, re-predict the re-bidding power execution sequence of the remaining time period set according to the re-joint bidding optimization model, and perform power fine-tuning processing on the energy storage device according to the re-bidding power execution sequence. The quotation execution plan optimization module is used to calculate the execution deviations of various revenues, costs, and plans after each day's operation, and to process the statistical data to achieve adaptive updates of the parameters of each optimization model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the energy storage power joint bidding method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the energy storage power joint bidding method as described in any one of claims 1-7.

Images