Multi-physical constraint charging and discharging pre-planning and spot quotation strategy generation method and system

Abstract

The application discloses a kind of multi-physical constraint charge-discharge pre-planning and spot quotation strategy generation method and system, it is related to energy storage power station electric power transaction auxiliary decision-making technical field, comprising: obtaining the heterogeneous state data of gas energy storage power station about target day, and establishing fine-grained time sequence state vector based on heterogeneous state data;Calculate the charging power of gas energy storage power station in charging stage, and simultaneously recursively charge recursive value of salt cavity state data and water tank state data and determine the end time of charging stage;Discharge recursive value of salt cavity state data and water tank state data is recursively calculated based on the discharge power of gas energy storage power station and the end time of discharge is determined;Establish the charge-discharge pre-planning table of gas energy storage power station;With spot quotation revenue maximization as objective function, the constrained optimization of charge-discharge and planning table is carried out, and the target charge-discharge declaration strategy is obtained.The application alleviates the technical problems that the existing technology cannot be adapted to the general energy storage quotation model and the operation characteristics of gas energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of power trading auxiliary decision-making technology for energy storage power stations, and in particular to a method and system for generating multi-physical-constrained charging and discharging pre-plans and spot price quotations. Background Technology

[0002] Existing power trading auxiliary decision-making systems for energy storage power stations typically use battery energy storage or general energy storage models as their targets. Based on parameters such as day-ahead forecast prices, energy storage capacity, upper limits of charge and discharge power, state of charge (SOC), or remaining capacity, they generate bidding suggestions for low-price charging and high-price discharging. Some solutions iteratively optimize bidding parameters through interactive bidding models and market clearing models. Other existing technologies aggregate multiple energy storage, wind power, photovoltaic, or adjustable load resources into a virtual power plant, coordinating multi-level power market positions and bidding strategies through cloud platforms or edge platforms. These solutions focus on the economic optimization and market clearing coordination of multiple entities and resources, but do not involve modeling the physical constraints at the single-station equipment level. They cannot characterize the coupling constraints between the salt chamber pressure and the hot and cold water thermal storage systems of compressed gas energy storage power stations, and the bidding plans generated based on optimal market returns may be unexecutable on the equipment side.

[0003] For compressed air energy storage (CAS) power plants, the actual charge and discharge capacity is affected by factors such as salt cavern pressure, changes in injection and production header pressure, power characteristics of the compression / expansion process, water volume in the hot and cold water storage systems, ambient temperature, and unit start-up and shutdown intervals. Existing CAS energy storage technologies mainly focus on salt cavern constant pressure design, compression / expansion unit control, and storage system structure, with less emphasis on methods for generating executable pricing plans. Furthermore, existing general-purpose energy storage pricing models cannot adapt to the gas-heat-water multi-media coupling operation characteristics of CAS power plants. Summary of the Invention

[0004] To address the aforementioned technical problems in the existing technology, embodiments of the present invention provide a method and system for generating multi-physical-constraint charge / discharge pre-planning and spot pricing strategies. The technical solution is as follows: On one hand, a method for generating multi-physical-constraint charge / discharge pre-planning and spot pricing strategies is provided, applied to compressed air energy storage power stations. The method includes: acquiring heterogeneous state data of the compressed air energy storage power station for a target date, and establishing a fine-grained time-series state vector based on the heterogeneous state data; the heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, brine chamber state data, and water tank state data; calculating the charging power of the compressed air energy storage power station during the charging phase based on the time-series state vector, and simultaneously recursively calculating the charging recursion values ​​of the brine chamber state data and the water tank state data; and determining the charging stage through the charging recursion values ​​and multi-constraint parallel boundary tracking. The end time of the charging phase; based on the end time of the charging phase and the preset switching interval, the initial time of the discharge phase of the compressed gas energy storage power station is determined; based on the discharge power of the compressed gas energy storage power station, the discharge recursion value of the salt chamber state data and the water tank state data is recursively derived, and the end time of discharge is determined by the discharge recursion value and multi-constraint parallel boundary tracking; based on the end time of the charging phase, the initial time of the discharge phase, the end time of the discharge phase, and the time-series state vector corresponding to each time point, a charging and discharging pre-planning table of the compressed gas energy storage power station is established; with the objective function of maximizing spot price revenue, the charging and discharging and the planning table are subjected to constrained optimization to obtain the target charging and discharging declaration strategy.

[0005] Optionally, the real-time operating status of the compressed gas energy storage power station includes the charging power and discharging power, remaining power generation capacity or unit power at time t on the target day; the salt chamber status data includes the salt chamber pressure; and the water tank status data includes the water volume in the high-temperature water tank and the water volume in the low-temperature water tank.

[0006] Optionally, the heterogeneous state data includes hourly forecast data for the target day; establishing a fine-grained time-series state vector based on the heterogeneous state data includes: interpolating the hourly forecast data using a linear interpolation method to obtain a quarter-hour level fine-grained time-series state vector.

[0007] Optionally, the formula for calculating the charging power of the compressed gas energy storage power station during the charging phase includes: Pch(t)=α×Temp(t)+β×Ps(t)+γ In the formula, Pch(t) is the charging power of the compressed air energy storage power station at time t, α, β, and γ are fitting coefficients, Temp(t) is the predicted temperature of the compressed air energy storage power station at time t, and Ps(t) is the salt chamber pressure at time t. The charging recursive value includes the brine chamber pressure and the volume of water in the cryogenic water tank during the charging stage; the recursive formula for the brine chamber pressure during the charging stage includes: Ps(t+1)=Ps(t)+rch×Δt In the formula, rch is the rate of change of salt chamber pressure during the charging stage determined based on the charging power, and Δt is the recursive time step. The end time of the charging stage is determined by the charging recursion value and multi-constraint parallel boundary tracking, including: taking the earlier of the times when the salt chamber pressure reaches the upper limit of the salt chamber pressure and when the water volume of the cryogenic water tank reaches the lower limit of the cryogenic water tank as the end time of the charging stage.

[0008] Optionally, the discharge recursive value includes the brine chamber pressure and the water volume in the high-temperature water tank during the discharge stage; the recursive formula for the brine chamber pressure during the discharge stage includes: Ps(t+1)=Ps(t)-rdis×Δt In the formula, Ps(t) is the salt cavity pressure at time t, rdis is the rate of change of salt cavity pressure during the discharge stage determined based on the discharge power, and Δt is the recursive time step. The discharge end time is determined by the discharge recursion value and multi-constraint parallel boundary tracking, including: taking the earlier of the times when the salt chamber pressure reaches the lower limit of the salt chamber pressure and the times when the water volume in the high-temperature water tank both reach the lower limit of the water volume in the high-temperature water tank as the discharge end time.

[0009] Optionally, the spot price revenue includes revenue per kilowatt-hour, and the formula for calculating the revenue per kilowatt-hour includes: Revenue per kilowatt-hour = Target revenue / Discharged electricity Target revenue = Estimated revenue - Estimated expenditure + Policy-related revenue such as grid connection and grid connection electricity compensation Estimated revenue = Σ(Discharge time Price(t) × Pdis(t) × Δt) Estimated expenditure = Σ(charging time Price(t) × Pch(t) × Δt) In the formula, Price(t) is the predicted price at time t, Pch(t) and Pdis(t) are the charging power and discharging power at time t, respectively, and Δt is the recursive time step.

[0010] On the other hand, a multi-physical constraint charge / discharge pre-planning and spot pricing strategy generation system is also provided to implement the multi-physical constraint charge / discharge pre-planning and spot pricing strategy generation method provided in the embodiments of the present invention. The system includes: a data acquisition module, a charging plan calculation module, a determination module, a discharge plan calculation module, an establishment module, and an optimization module. The data acquisition module is used to acquire heterogeneous state data of the compressed air energy storage power station for a target day, and to establish a fine-grained time-series state vector based on the heterogeneous state data. The heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, brine chamber state data, and water tank state data. The charging plan calculation module is used to calculate the charging power of the compressed air energy storage power station during the charging phase based on the time-series state vector, and to simultaneously recursively calculate the charging progress of the brine chamber state data and the water tank state data. The system employs a multi-constraint parallel boundary tracking method to determine the end time of the charging phase using the charging recursive value. A determination module determines the initial time of the discharge phase of the compressed gas energy storage power station based on the end time of the charging phase and a preset switching interval. A discharge plan calculation module recursively calculates the discharge recursive value of the salt chamber state data and the water tank state data based on the discharge power of the compressed gas energy storage power station, and determines the end time of the discharge using the discharge recursive value and multi-constraint parallel boundary tracking. A setup module establishes a pre-planned charging and discharging schedule for the compressed gas energy storage power station based on the end time of the charging phase, the initial time of the discharge phase, the end time of the discharge phase, and the time-series state vectors corresponding to each time point. An optimization module performs constrained optimization on the charging and discharging schedule with the objective function of maximizing spot price revenue to obtain a target charging and discharging declaration strategy.

[0011] On the other hand, an electronic device is also provided, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0012] On the other hand, a computer-readable storage medium is also provided, which stores computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0013] This invention provides a method and system for generating multi-physical-constrained charge / discharge pre-plans and spot pricing strategies. Based on multi-dimensional states such as salt chamber pressure, hot and cold water tank volume, predicted temperature, charge / discharge power, and remaining power, the physical executable boundary envelope of the charge / discharge plan is solved through discrete-time state recursion. Furthermore, constrained optimization is performed with the goal of maximizing profits, ensuring that the output spot bidding plan satisfies the physical executability of the compressed air storage unit while achieving optimal profits. This reduces the risks of manual calculations and unexecutable bids, and alleviates the technical problem of existing general energy storage pricing models being incompatible with the operating characteristics of compressed air storage power stations. Attached Figure Description

[0014] 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.

[0015] Figure 1 A flowchart illustrating a method for generating multi-physical-constraint charge / discharge pre-planning and spot pricing strategies, provided in an embodiment of the present invention; Figure 2 A schematic diagram of a two-stage structure provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of multi-constraint parallel boundary tracking and end-time determination during the charging phase of a compressed air energy storage power station provided in an embodiment of the present invention. Figure 4 A flowchart of a revenue optimization process with multiple physical feasibility constraints provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a multi-physical constraint charge and discharge pre-planning and spot price quotation strategy generation system provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Figure 1 This is a flowchart illustrating a method for generating multi-physical-constraint charge / discharge pre-planning and spot pricing strategies according to an embodiment of the present invention. This method is applied to compressed air energy storage power plants. Figure 1 As shown, the method specifically includes the following steps: Step S102: Obtain heterogeneous state data of the compressed air energy storage power station for the target day, and establish a fine-grained time-series state vector based on the heterogeneous state data; the heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, salt chamber state data, and water tank state data.

[0018] Preferably, the real-time operating status of the compressed air energy storage power station includes the charging power and discharging power, remaining power generation capacity or unit power at time t on the target day; the salt chamber status data includes the salt chamber pressure; and the water tank status data includes the water volume of the high-temperature water tank and the water volume of the low-temperature water tank.

[0019] In one optional embodiment of the present invention, the heterogeneous state data includes hourly forecast data for the target day; establishing a fine-grained time-series state vector based on the heterogeneous state data includes: interpolating the hourly forecast data using a linear interpolation method to obtain a fine-grained time-series state vector at the quarter-hour level.

[0020] Step S104: Calculate the charging power of the compressed gas energy storage power station during the charging phase based on the time-series state vector, and simultaneously recursively calculate the charging recursion values ​​of the salt chamber state data and water tank state data. Determine the end time of the charging phase by using the charging recursion values ​​and multi-constraint parallel boundary tracking.

[0021] Specifically, the charging recursion values ​​include the salt chamber pressure and the volume of water in the cryogenic water tank during the charging phase.

[0022] Specifically, the earlier of the times when the salt chamber pressure reaches the upper limit of the salt chamber pressure and when the water volume in the cryogenic water tank reaches the lower limit of the cryogenic water tank volume is taken as the end time of the charging phase.

[0023] Step S106: Determine the initial time of the discharge phase of the compressed gas energy storage power station based on the end time of the charging phase and the preset switching interval.

[0024] Step S108: Based on the discharge power recursion data of the compressed gas energy storage power station, the discharge recursion value of the salt chamber state data and the water tank state data is derived, and the discharge end time is determined by the discharge recursion value and multi-constraint parallel boundary tracking.

[0025] Specifically, the discharge recursive values ​​include the salt chamber pressure and the water volume in the high-temperature water tank during the discharge phase.

[0026] Specifically, the earlier of the two times, when the salt chamber pressure reaches the lower limit of the salt chamber pressure during the discharge phase and when the water volume in the high-temperature water tank reaches the lower limit of the high-temperature water tank volume, is taken as the end time of the discharge phase.

[0027] Step S110: Based on the end time of the charging phase, the beginning time of the discharging phase, the end time of the discharging phase, and the time sequence state vectors corresponding to each time point, establish a pre-planned charging and discharging schedule for the compressed gas energy storage power station.

[0028] Step S112: Using the maximization of spot price revenue as the objective function, perform constrained optimization on the charging / discharging schedule and the schedule to obtain the target charging / discharging declaration strategy.

[0029] Figure 2 This is a schematic diagram of a two-stage structure provided according to an embodiment of the present invention. Figure 2 As shown, the present invention adopts a two-stage architecture of "physical executability first, market pricing later", which is fundamentally different from the single-stage mode of the prior art that "focuses on market revenue optimization and is supplemented by simple power / SOC constraints".

[0030] Specifically, in the method provided in this embodiment of the invention, the first stage—multi-physics executable envelope solution—takes the operating day D as the target day, divides the period from 00:15 to 24:00 on day D into 96 15-minute time points, and obtains the real-time operating status of the compressed gas energy storage power station at 0:00 on day D, the predicted temperature on day D, and the upper and lower limits of the salt chamber pressure configured by the user, the lower limit of the water volume of the high and low temperature water tanks, the pressure change rate, the hot and cold water consumption parameters, the discharge power, and other parameters. A heterogeneous state vector is established for each time point and recursively derived according to the charging / discharging stage. Charging phase: The charging power is calculated based on the interpolated temperature and salt chamber pressure, and the salt chamber pressure rises, the water volume of low temperature tanks A / B / C / D decreases, and the water volume of high temperature tanks increases simultaneously. Through multi-constraint parallel boundary tracking, the upper limit boundary of pressure and the lower limit boundary of low temperature tank water volume are determined respectively, and the effective moment before the earlier failure boundary is taken as the charging end moment.

[0031] Discharge phase: The decrease in salt chamber pressure, the decrease in water volume in high-temperature water tanks A / B / C / D, and the increase in low-temperature water volume are deduced based on the discharge power; the discharge end time is also determined by multi-constraint parallel boundary tracking.

[0032] The second stage (application layer) – profit optimization with physical feasibility constraints: Based on the executable envelope of the first stage, optimization is performed with profit maximization as the objective function. Unlike existing technologies where "optimization constraints are only the upper and lower limits of battery power and charge / discharge flags," this invention, in each optimization iteration, starts with the initial physical values ​​at day D0 of the first stage, such as the salt chamber pressure, the water volume in the A / B / C / D sub-tanks, and the remaining power. It then re-calculates using the same linear formula for charging power, recursive calculations and upper limits for salt chamber pressure, the rate of change of the cold and hot water tanks, and the discharge stage constraints as in the first stage. The optimal profit is searched only within the physically executable candidate solution set, thus obtaining a charge / discharge application plan that is both profit-optimal and satisfies the physical characteristics of the compressed gas storage unit.

[0033] Consistency Guarantee Mechanism: When a user edits a power curve or imports an external plan, the system does not use the pressure, water volume, and power data from the original plan. Instead, it re-executes the same set of state recursion logic based on the imported or edited start and end points of charging and discharging, automatically updating all physical states and strategy results.

[0034] A specific implementation method provided by this invention is as follows: Step 1: Establish a 15-minute fine-grained time series and heterogeneous state vector: Obtain the running day D (i.e., the target day), and establish 96 time points from 00:15, 00:30, 00:45, 01:00...24:00 on day D. Establish the time-series state vector X(t) for each time point t pair: X(t)={Temp(t),Ps(t),Pch(t),Pdis(t),E(t),H_A(t),H_B(t),H_C(t),H_D(t),L_A(t),L_B(t),L_C(t),L_D(t),Price(t)} In the formula, Temp(t) is the predicted temperature of the compressed air energy storage power station at time t, Ps(t) is the salt chamber pressure at time t, Pch(t) is the charging power of the compressed air energy storage power station at time t, Pch(t) and Pdis(t) are the charging power and discharging power at time t, respectively, E(t) is the remaining power generation capacity or unit power at time t, H_A(t), H_B(t), H_C(t), and H_D(t) are the water volume of the high-temperature water tank at time t, where A, B, C, and D represent the numbers of the high-temperature water tanks, respectively; L_A(t), L_B(t), L_C(t), and L_D(t) are the water volume of the low-temperature water tank at time t, where A, B, C, and D represent the numbers of the low-temperature water tanks, respectively, and Price(t) is the predicted price at time t.

[0035] In this embodiment of the invention, the time-series state vector includes pressure (MPa) and water volume (m³). 3 Heterogeneous physical quantities such as power (MW) and electricity (MWh) are uniformly mapped onto a time grid consistent with the spot market declaration, forming a discrete-time state model dedicated to compressed gas energy storage.

[0036] Step 2: Initialize the state at 0:00 on Day D: Read the real-time operating parameters at 0:00 on day D-1 or the most recently available time, and use them as the initial state values ​​for operating day D: The initial pressure of salt chamber No. 1 is derived from real-time operational data; The initial water volume of high-temperature / low-temperature water tanks A / B / C / D is obtained from DCS measuring points; The initial remaining power is adjusted based on the previous day's executed charge / discharge plan or real-time measurement points.

[0037] In a preferred embodiment, the status at 0:00 on day D can be superimposed with the impact of the previous day's clearing plan on top of the real-time values: the water volume in the low-temperature water tank is superimposed with the charging time, and the high-temperature water volume increases or decreases accordingly according to the consumption rate of the low-temperature water, so as to ensure the continuity of the status across days.

[0038] Step 3: Coupled calculation of temperature interpolation and charging power: The system obtains the hourly predicted temperature for operating day D. Linear interpolation is used for the three 15-minute intervals between the hour: Temp(k,m)=Temp(k)+m×[Temp(k+1)-Temp(k)] / 4,m=1,2,3 The charging power is calculated based on the interpolated temperature and salt chamber pressure, forming a closed-loop coupling of temperature, pressure, and power. Pch(t)=α×Temp(t)+β×Ps(t)+γ In the formula, Pch(t) represents the charging power of the compressed air energy storage power station at time t, α, β, and γ are fitting coefficients, which can be determined by fitting the equipment characteristic curve or by personnel configuration; Temp(t) represents the predicted temperature of the compressed air energy storage power station at time t, and Ps(t) represents the salt chamber pressure at time t. For example, Pch(t) = 0.22 × Temp(t) + 3.62 × Ps(t) + 112.30. The charging power affects the salt chamber pressure recursion rate and the charging time, constituting the dynamic feasible region.

[0039] Step 4: Multi-physical state recursion and multi-constraint boundary determination during the charging phase: Starting from the initial moment of charging, the salt chamber pressure during the charging phase is calculated in 15-minute increments: Ps(t+1)=Ps(t)+rch×Δt In the formula, rch is the rate of change of salt chamber pressure during the charging stage determined based on the charging power, and Δt is the recursive time step.

[0040] Low-temperature water consumption: If QL is the total low-temperature water consumption per hour, then the consumption of each low-temperature water tank in every 15 minutes is QL / (4×4) (the first 4 represents four low-temperature water tanks, and the second 4 represents four 15-minute time intervals per hour). Low-temperature water tanks A / B / C / D are deducted respectively, and high-temperature water consumption is increased according to the heat exchange ratio.

[0041] The present invention tracks two charging feasibility boundaries in parallel as follows: Pressure boundary t_P: The last moment that satisfies Ps(t)≤Psmax is used as the upper bound of pressure feasibility; where Psmax is the upper limit of salt cavity pressure. Water volume boundary t_L: The last moment when L_A / L_B / L_C / L_D are all ≥ Wmin is taken as the feasible upper limit of the water volume of the sub-tank; where Wmin is the lower limit of the water volume of the low temperature water tank.

[0042] The end time of the charging phase is the effective time point preceding min(t_P, t_L). The core of this step lies in parallel tracking the failure boundaries of heterogeneous physical quantities such as pressure and the water volume of the four sub-tanks, and taking the earliest failure point to solve for the physically executable envelope of the charging process, ensuring that the charging plan simultaneously satisfies both pressure and sub-tank water volume constraints. The determination process is as follows: Figure 3 As shown. Among them, Figure 3 This is a schematic diagram of multi-constraint parallel boundary tracking and end time determination during the charging phase of a compressed air energy storage power station according to an embodiment of the present invention.

[0043] Step 5: Multi-physical state recursion and multi-constraint boundary determination during the discharge stage: The initial time of the discharge phase is determined based on the end time of the charging phase and the preset switching interval, with a preferred preset switching interval of 1 hour. The discharge phase salt chamber pressure is calculated in 15-minute increments. Ps(t+1)=Ps(t)-rdis×Δt In the formula, Ps(t) is the salt cavity pressure at time t, rdis is the rate of change of salt cavity pressure during the discharge stage determined based on the discharge power, and Δt is the recursive time step. High-temperature water consumption: If QH is the total high-temperature water consumption per hour, then the consumption of each high-temperature water tank every 15 minutes is QH / (4×4). High-temperature water tanks A / B / C / D are deducted respectively, and the low-temperature water consumption is increased according to the heat exchange ratio.

[0044] The feasible boundaries for parallel tracking of two discharges in this invention are as follows: Pressure boundary t_P': The last moment that satisfies Ps(t)≥Psmin is taken as the lower bound of pressure feasibility; where Psmin is the lower limit of salt cavity pressure. Water volume boundary t_H: The last moment when H_A / H_B / H_C / H_D are all ≥ Wmin is used as the feasible lower bound of the water volume of the sub-tank; where Wmin is the lower limit of the water volume of the low temperature water tank.

[0045] The end time of the discharge phase is the effective time point before min(t_P',t_H).

[0046] Step 6: Output a physically executable charge / discharge pre-plan: Based on the start and end times of charging and discharging and the state vectors at each time point, this invention outputs a 96-point pre-planned charging and discharging schedule, which includes at least: time, temperature, salt chamber pressure, power, electricity, high-temperature water volume, low-temperature water volume, water volume of high-temperature water tanks A / B / C / D, and water volume of low-temperature water tanks A / B / C / D.

[0047] Step 7: Optimizing Profits and Generating Spot Trading Strategies with Physical Feasibility Constraints Based on the physically executable envelope of the first stage, constrained optimization is performed with revenue maximization as the objective function. Preferably, the revenue from spot pricing includes revenue per kilowatt-hour, and the formula for calculating revenue per kilowatt-hour includes: Revenue per kilowatt-hour = Target revenue / Discharged electricity Target revenue = Estimated revenue - Estimated expenditure + Policy-related revenue such as grid connection and grid connection electricity compensation Estimated revenue = Σ(Discharge time Price(t) × Pdis(t) × Δt) Estimated expenditure = Σ(charging time Price(t) × Pch(t) × Δt) In the formula, Price(t) is the predicted price at time t, Pch(t) and Pdis(t) are the charging power and discharging power at time t, respectively, and Δt is the recursive time step.

[0048] The degrees of freedom for optimization on both the charging and discharging sides are fundamentally different, which is the key difference between compressed gas energy storage and battery energy storage: On the charging side – power is uncontrollable: The charging power of the compressed gas energy storage unit is physically determined by the predicted temperature and salt chamber pressure via Pch(t) = α × Temp(t) + β × Ps(t) + γ, and cannot be freely set like a battery. The charging power, especially during the initial 1-3 15-minute periods, is extremely uncontrollable. Therefore, the reporting algorithm does not adjust the power value of the input charging plan. It only discards data based on constraints such as salt chamber pressure, water volume in the high and low temperature tanks, and remaining power, starting from the end of charging and proceeding forward through each period to ensure a physically executable charging plan is output while maximizing revenue (this reverse discarding is consistent with the determination of the earliest valid point before the earliest failure boundary in step 4).

[0049] Discharge side - Configurable power: The discharge power can be set by the user (e.g., 200MW) or determined by the dispatch command. The optimal discharge period and start and end points are selected to maximize benefits while meeting the lower limit of the brine chamber pressure and the lower limit of the water volume of the high temperature water tank.

[0050] Feasibility constraints are applied during the optimization process as follows: Each optimization iteration starts with the initial physical values ​​at 0:00 on day D of the first stage, including the salt chamber pressure, water volume in tanks A / B / C / D, and remaining electricity. The physical recursion and constraints from steps 3-5 are recalculated. Candidate solutions that trigger pressure or water volume limits in any tank are deemed unexecutable and removed from the solution set. Only physically executable candidate solutions are compared. The optimization process is as follows: Figure 4 As shown.

[0051] Preferably, the method provided in this embodiment of the invention further includes: when a user edits the power curve, modifies the charge / discharge start and end points, modifies constraint parameters, or imports an external plan file, the system triggers a full-state recalculation: using the new charge / discharge start and end points as input, the state recursion logic of steps 2-7 is re-executed, without directly using the pressure, water volume, and power data from the imported file. Through this mechanism, the system automatically generates plans, manually edits plans, and imports plans are all subject to the same set of physical constraint verification, avoiding manual adjustments that bypass equipment boundaries.

[0052] Example 1: Generation of a standard operating day's charge and discharge schedule: The system is set to run on day D. It reads the pressure in salt chamber 1, the water volume in cryogenic tanks A / B / C / D, and the water volume in high-temperature tanks A / B / C / D at 00:00 the day before D, and obtains the hourly predicted temperature and the day-ahead predicted price for day D. The system interpolates the hourly temperature into a 96-point temperature sequence, initializes a 96-point status table, and calculates the charging power point by point starting from the user-set initial charging time. If the salt chamber pressure at t=18 does not exceed 7.5MPa, but the water volume in cryogenic tank C is below 100m³, the system will proceed as follows: 3 If the lower limit is met, the system will not extend the charging time further, and t=17 will be determined as the charging end time. Conversely, if all four tanks of cryogenic water meet the lower limit, but the salt chamber pressure will exceed the upper limit at the next moment, the time point before pressure failure will also be taken as the charging end time. After charging is completed, the initial discharge time is determined according to a 1-hour switching interval, and the pressure drop and high-temperature water consumption during the discharge process are calculated point by point. If the high-temperature water tank B bottoms out at t=32 or the salt chamber pressure is below 5.7MPa at t=35, the effective time point before the earliest failure boundary will be taken as the discharge end time. The system summarizes and outputs a 96-point pre-plan table, and calculates the estimated revenue in combination with the day-ahead forecast price.

[0053] Example 2: Full state recalculation after importing external plans: The user imports an external charge / discharge plan file, with the charging period from 02:00 to 08:00 and the discharging period from 10:00 to 16:00. After reading the start and end points and power curve from the imported file, the system does not use the brine chamber pressure and water volume data attached to the file. Instead, it uses the real-time measurement point at 0:00 on day D as the initial value and recalculates the pressure and water volume according to the imported start and end points. If the recalculation result shows that the brine chamber pressure will exceed the upper limit when charging reaches 08:00, the system automatically prompts that the imported plan cannot be executed on the equipment side and provides a suggestion for the maximum possible end time of charging that meets the constraints.

[0054] As described above, the embodiments of the present invention provide a method for generating multi-physical-constrained charge and discharge pre-plans and spot pricing strategies. Based on multi-dimensional states such as salt chamber pressure, hot and cold water tank volume, predicted temperature, charge and discharge power, and remaining power, the method solves the physical executable boundary envelope of the charge and discharge plan through discrete-time state recursion. On this basis, it further performs constrained optimization with the goal of maximizing profits, so that the output spot bidding plan always satisfies the physical executability of the compressed gas storage unit while achieving optimal profits. This reduces the risks of manual trial calculations and unexecutable bids, and alleviates the technical problem that the general energy storage pricing model in the prior art cannot be adapted to the operating characteristics of the compressed gas storage power station.

[0055] Figure 5 This is a schematic diagram of a multi-physical-constraint charge / discharge pre-planning and spot price quotation strategy generation system provided by an embodiment of the present invention. Figure 5 As shown, the system includes: a data acquisition module 10, a charging plan calculation module 20, a determination module 30, a discharge plan calculation module 40, an establishment module 50, and an optimization module 60.

[0056] Specifically, the data acquisition module 10 is used to acquire heterogeneous state data of the compressed air energy storage power station for the target day, and to establish a fine-grained time-series state vector based on the heterogeneous state data; the heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, salt chamber state data and water tank state data; The charging plan calculation module 20 is used to calculate the charging power of the compressed gas energy storage power station during the charging phase based on the time-series state vector, and simultaneously recursively calculate the charging recursion value of the salt chamber state data and water tank state data. The charging recursion value and multi-constraint parallel boundary tracking are used to determine the end time of the charging phase. The determination module 30 is used to determine the initial time of the discharge phase of the compressed air energy storage power station based on the end time of the charging phase and the preset switching interval. The discharge plan calculation module 40 is used to deduce the discharge recursion value based on the discharge power recursion data of the salt chamber and the water tank based on the discharge power of the compressed gas energy storage power station, and determine the discharge end time by using the discharge recursion value and multi-constraint parallel boundary tracking. Module 50 is established to create a pre-planned charging and discharging schedule for the compressed gas energy storage power station based on the end time of the charging phase, the beginning time of the discharging phase, the end time of the discharging phase, and the time-series state vectors corresponding to each time point. The optimization module 60 is used to perform constrained optimization of the charging and discharging schedule with the objective function of maximizing the spot price profit, so as to obtain the target charging and discharging declaration strategy.

[0057] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0058] The present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0059] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0060] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for generating multi-physical-constraint charge / discharge pre-planning and spot pricing strategies, characterized in that, Applied to compressed air energy storage power stations; the method includes: Obtain heterogeneous state data of the compressed air energy storage power station for the target day, and establish a fine-grained time-series state vector based on the heterogeneous state data; the heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, brine chamber state data, and water tank state data; The charging power of the compressed gas energy storage power station during the charging phase is calculated based on the time-series state vector, and the charging recursion value of the salt chamber state data and the water tank state data is simultaneously recursively calculated. The end time of the charging phase is determined by the charging recursion value and multi-constraint parallel boundary tracking. Based on the end time of the charging phase and the preset switching interval, the initial time of the discharge phase of the compressed gas energy storage power station is determined. Based on the discharge power of the compressed gas energy storage power station, the discharge recursion value of the salt chamber state data and the water tank state data is recursively derived, and the discharge end time is determined by the discharge recursion value and multi-constraint parallel boundary tracking. Based on the end time of the charging phase, the beginning time of the discharging phase, the end time of the discharging phase, and the time sequence state vectors corresponding to each time point, a charging and discharging pre-planning table for the compressed gas energy storage power station is established. The target charging and discharging strategy is obtained by performing constrained optimization on the charging and discharging schedule with the objective function of maximizing spot price revenue.

2. The method according to claim 1, characterized in that: The real-time operating status of the compressed gas energy storage power station includes the charging power and discharging power, remaining power generation capacity or unit power at time t on the target day; the salt chamber status data includes the salt chamber pressure; the water tank status data includes the water volume of the high-temperature water tank and the water volume of the low-temperature water tank.

3. The method according to claim 1, characterized in that: The heterogeneous state data includes hourly forecast data for the target day; Establishing a fine-grained time-series state vector based on the heterogeneous state data includes: interpolating the hourly prediction data using a linear interpolation method to obtain a fine-grained time-series state vector at the quarter-hour level.

4. The method according to claim 1, characterized in that: The formula for calculating the charging power of the compressed air energy storage power station during the charging phase includes: Pch(t)=α×Temp(t)+β×Ps(t)+γ In the formula, Pch(t) is the charging power of the compressed air energy storage power station at time t, α, β, and γ are fitting coefficients, Temp(t) is the predicted temperature of the compressed air energy storage power station at time t, and Ps(t) is the salt chamber pressure at time t. The charging recursive value includes the brine chamber pressure and the volume of water in the cryogenic water tank during the charging stage; the recursive formula for the brine chamber pressure during the charging stage includes: Ps(t+1)=Ps(t)+rch×Δt In the formula, rch is the rate of change of salt chamber pressure during the charging stage determined based on the charging power, and Δt is the recursive time step. The end time of the charging stage is determined by the charging recursion value and multi-constraint parallel boundary tracking, including: taking the earlier of the times when the salt chamber pressure reaches the upper limit of the salt chamber pressure and when the water volume of the cryogenic water tank reaches the lower limit of the cryogenic water tank as the end time of the charging stage.

5. The method according to claim 1, characterized in that: The discharge recursive value includes the salt chamber pressure and the water volume in the high-temperature water tank during the discharge phase. The recursive formula for the salt cavity pressure during the discharge phase includes: Ps(t+1)=Ps(t)-rdis×Δt In the formula, Ps(t) is the salt cavity pressure at time t, rdis is the rate of change of salt cavity pressure during the discharge stage determined based on the discharge power, and Δt is the recursive time step. The discharge end time is determined by the discharge recursion value and multi-constraint parallel boundary tracking, including: taking the earlier of the times when the salt chamber pressure reaches the lower limit of the salt chamber pressure and the times when the water volume in the high-temperature water tank both reach the lower limit of the water volume in the high-temperature water tank as the discharge end time.

6. The method according to claim 1, characterized in that: The spot price revenue includes revenue per kilowatt-hour, and the calculation formula for revenue per kilowatt-hour includes: Revenue per kilowatt-hour = Target revenue / Discharged electricity Target revenue = Estimated revenue - Estimated expenditure + Policy-related revenue such as grid connection and grid connection electricity compensation Estimated revenue = Σ(Discharge time Price(t) × Pdis(t) × Δt) Estimated expenditure = Σ(charging time Price(t) × Pch(t) × Δt) In the formula, Price(t) is the predicted price at time t, Pch(t) and Pdis(t) are the charging power and discharging power at time t, respectively, and Δt is the recursive time step.

7. A multi-physical-constraint charge / discharge pre-planning and spot price quotation strategy generation system, characterized in that, The system is used to implement the multi-physical constraint charge / discharge pre-planning and spot pricing strategy generation method according to any one of claims 1-6; the system includes: a data acquisition module, a charging plan calculation module, a determination module, a discharge plan calculation module, a setup module, and an optimization module; wherein... The data acquisition module is used to acquire heterogeneous state data of the compressed air energy storage power station for a target day, and to establish a fine-grained time-series state vector based on the heterogeneous state data; the heterogeneous state data includes the real-time operating status of the compressed air energy storage power station, predicted temperature, predicted price, brine chamber state data, and water tank state data; The charging plan calculation module is used to calculate the charging power of the compressed gas energy storage power station during the charging phase based on the time-series state vector, and simultaneously recursively calculate the charging recursion value of the salt chamber state data and the water tank state data, and determine the end time of the charging phase through the charging recursion value and multi-constraint parallel boundary tracking. The determining module is used to determine the initial time of the discharge phase of the compressed gas energy storage power station based on the end time of the charging phase and the preset switching interval. The discharge plan calculation module is used to recursively deduce the discharge recursion value of the salt chamber state data and the water tank state data based on the discharge power of the compressed gas energy storage power station, and determine the discharge end time by using the discharge recursion value and multi-constraint parallel boundary tracking. The establishment module is used to establish a charging and discharging pre-plan table for the compressed gas energy storage power station based on the end time of the charging phase, the beginning time of the discharging phase, the end time of the discharging phase, and the time sequence state vector corresponding to each time point. The optimization module is used to perform constrained optimization on the charging and discharging schedule with the objective function of maximizing spot price revenue, so as to obtain the target charging and discharging declaration strategy.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-6.

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