Pumped storage coupled compressed air energy storage scheduling method

Abstract

The invention relates to a pumped storage and compressed air energy storage coupled scheduling method, which comprises the following steps of: establishing a multi-stage data system based on input data of multiple time scales; constructing a multi-time scale scheduling model; when the load of the pumped storage mixed compressed air energy storage power station is lower than a first preset threshold value, electricity is abandoned preferentially, water pumping and compression are conducted, and when the load is larger than a second preset threshold value, the pumped storage mixed compressed air energy storage power station is controlled to conduct combined energy release. The nonlinear relation between the pumped storage efficiency and the water level and the nonlinear relation between the compressed air energy storage power and the pressure are converted into linear constraints, solving is conducted by dividing the water level interval and the pressure level, and a scheduling strategy is obtained. Therefore, the problems of relatively low energy storage efficiency, insufficient scheduling refinement, relatively high system standby cost and the like caused by the problems that pumped storage water scheduling and CAES energy management are separated, a standby capacity coordination mechanism is lacked and the like in a hybrid system scheduling method in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of power system dispatching and energy storage technology, and in particular to a pumped storage coupled compressed air energy storage dispatching method. Background Technology

[0002] With the large-scale integration of new energy sources such as wind power and photovoltaics into the power grid, their intermittency and volatility have intensified the pressure on the power grid to regulate peak loads and increased the demand for reserve capacity. A single energy storage system is insufficient to meet the demand for grid integration.

[0003] Among related technologies, pumped hydro storage's rapid start-up and shutdown and large-capacity regulation complement CAES (Compressed Air Energy Storage)'s large-capacity storage and long lifespan. By constructing a hybrid energy storage system combining pumped hydro storage and CAES, the advantages of peak-shaving capacity and energy storage duration can be effectively integrated, providing flexible regulation and stable support for high-proportion renewable energy power grids, thus becoming an important direction for solving this problem.

[0004] However, in related technologies, the hybrid system scheduling method has problems such as the separation of pumped storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and lack of backup capacity coordination mechanism, resulting in low energy storage efficiency, insufficient scheduling refinement, and high system backup costs, which urgently need to be improved. Summary of the Invention

[0005] This application provides a pumped-storage coupled compressed-air energy storage scheduling method to solve the problems in related technologies, such as the separation of pumped-storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and lack of backup capacity coordination mechanism, which lead to low energy storage efficiency, insufficient scheduling refinement, and high system backup costs.

[0006] The first aspect of this application provides a pumped-storage coupled compressed-air energy storage scheduling method, comprising the following steps: establishing a multi-level data system based on multi-timescale input data; constructing a multi-timescale scheduling model using the multi-timescale data system; based on the multi-timescale scheduling model, when the load of the pumped-storage coupled compressed-air energy storage power station is lower than a first preset threshold, priority is given to power curtailment and pumping and compression are performed, and when the load is greater than a second preset threshold, the pumped-storage coupled compressed-air energy storage power station is controlled to perform joint energy release, wherein the nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure are transformed into linear constraints, and solved by dividing water level intervals and pressure levels to obtain the pumped-storage coupled compressed-air energy storage scheduling strategy.

[0007] This application embodiment can achieve full-process optimization of energy storage systems through the synergy of a multi-timescale data system and a scheduling model. The multi-level data system accurately captures changes in renewable energy output, effectively improving the renewable energy absorption rate. The multi-timescale scheduling model hierarchically optimizes the objective function, significantly reducing system operating costs. Nonlinear constraints are transformed into linear constraints for solving, greatly improving scheduling decision efficiency. The synergistic energy release of pumped hydro storage and compressed air storage enhances grid stability and reliability, providing reliable technical support for high-proportion renewable energy grid integration. Optionally, in one embodiment of this application, establishing the multi-level data system includes: constructing multi-timescale input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, to obtain coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data respectively; and constructing a three-level multi-level data system based on the coarse-grained planning data, the high-precision correction data, and the ultra-short-term real-time data.

[0008] This application's embodiments can construct a three-tiered system by differentially collecting data at different time scales, maximizing the timeliness of predictive data. Coarse-grained planning data guides global energy balance, high-precision correction data optimizes equipment state trajectories, and ultra-short-term real-time data triggers emergency adjustments, improving the utilization efficiency of energy storage equipment and the flexibility of grid regulation, and providing accurate input for subsequent scheduling models.

[0009] Optionally, in one embodiment of this application, the construction of a multi-timescale scheduling model using the multi-level data system includes: designing a three-layer model of day-ahead planning, intraday correction, and real-time response based on the three-level multi-level data system, so as to formulate a basic plan with the goal of minimizing the total cost of the day, adjust the output allocation and limit the rate of state change based on hourly forecasts during the day, and generate control commands in real time using 15-minute ultra-short-term data to form a closed-loop control system.

[0010] The embodiments of this application can design a three-layer model with objective functions and constraints, realizing hierarchical and progressive optimization of the scheduling strategy. The goal of minimizing day-ahead costs balances energy storage operation and curtailment penalties; intraday adjustment cost optimization limits the rate of equipment state change; and real-time rapid adjustment ensures system frequency stability.

[0011] Optionally, in one embodiment of this application, the objective function of the three-layer model includes:

[0012] minf day-ahead =C PS +C CAES +C penalty ,

[0013] minf intraday =C PS-adj +C CAES-adj +Cpenalty ,

[0014] minf real-time =C fast-adjust +C penalty-real ,

[0015] Where, minf day-ahead The objective function is C. PS For pumped storage operation costs, C CAES For CAES operating costs, C penalty The cost of curtailing wind and solar power; minf intraday The objective function for the day is C. PS-adj To adjust the cost of pumped storage, C CAES-adj Adjust costs for CAES output; minf real-time It is a real-time objective function, C fast-adjust To allow for real-time and rapid cost adjustments.

[0016] The embodiments of this application can design a collaborative model of "day-to-day planning - intraday correction - real-time response", optimize the scheduling strategy layer by layer, and form a closed-loop control system of "global planning - local correction - instantaneous response".

[0017] Optionally, in one embodiment of this application, the nonlinear relationship between pumped storage efficiency and water level, and between compressed air storage power and pressure, is transformed into linear constraints and solved by dividing water level intervals and pressure levels. This includes: dividing the water volume of the pumped storage reservoir into multiple water level intervals during the nonlinear constraint solution process, wherein the relationship between power generation efficiency and water volume is approximated by a linear equation within each water level interval; defining the upper limit of expansion power according to the pressure level of the gas storage tank, and establishing a linear mapping between the pressure level and the upper limit of power for solution.

[0018] The embodiments of this application can improve the solution efficiency of the scheduling model, shorten the calculation time, enable the scheduling strategy to be generated and executed faster, provide reliable technical support for the optimized scheduling of energy storage systems, and reduce the computational cost of system operation.

[0019] A second aspect of this application provides a pumped-storage coupled compressed-air energy storage scheduling device, comprising: an establishment module for establishing a multi-level data system based on multi-timescale input data; a construction module for constructing a multi-timescale scheduling model using the multi-timescale data system; and a scheduling module for, based on the multi-timescale scheduling model, prioritizing power abandonment and pumping and compression when the load of the pumped-storage coupled compressed-air energy storage power station is lower than a first preset threshold, and controlling the pumped-storage coupled compressed-air energy storage power station to perform joint energy release when the load is greater than a second preset threshold. The nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure, is transformed into linear constraints, which are solved by dividing water level intervals and pressure levels to obtain a pumped-storage coupled compressed-air energy storage scheduling strategy.

[0020] Optionally, in one embodiment of this application, the establishment module includes: a data acquisition unit, used to construct multi-timescale data input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, to acquire coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data respectively; and a system construction unit, used to construct the three-level multi-level data system based on the coarse-grained planning data, the high-precision correction data, and the ultra-short-term real-time data.

[0021] Optionally, in one embodiment of this application, the building module includes: a control unit, used to design a three-layer model of day-ahead planning-intraday correction-real-time response based on the three-level multi-level data system, so as to formulate a basic plan with the goal of minimizing the total cost of the day, adjust the output allocation and limit the rate of state change based on hourly forecasts during the day, and generate control commands in real time using 15-minute ultra-short-term data to form a closed-loop control system.

[0022] Optionally, in one embodiment of this application, the objective function of the three-layer model includes:

[0023] minf day-ahead =C PS +C CAES +C penalty

[0024] minf intraday =C PS-adj +C CAES-adj +C penalty

[0025] minf real-time =C fast-adjust +C penalty-real

[0026] Where, minf day-ahead The objective function is C. PSFor pumped storage operation costs, C CAES For CAES operating costs, C penalty The cost of curtailing wind and solar power; minf intraday The objective function for the day is C. PS-adj To adjust the cost of pumped storage, C CAES-adj Adjust costs for CAES output; minf real-time It is a real-time objective function, C fast-adjust To allow for real-time and rapid cost adjustments.

[0027] Optionally, in one embodiment of this application, the scheduling module includes: a partitioning unit, used to divide the water volume of the pumped storage reservoir into multiple water level intervals during the nonlinear constraint solution process, wherein the relationship between power generation efficiency and water volume is approximated by a linear equation within each water level interval; and a mapping unit, used to define the upper limit of expansion power according to the pressure level of the gas storage tank, and establish a linear mapping between the pressure level and the upper limit of power for solution.

[0028] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pumped-storage coupled compressed-air energy storage scheduling method as described in the above embodiments.

[0029] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the pumped-storage coupled compressed-air energy storage scheduling method described above.

[0030] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the pumped-storage coupled compressed-air energy storage scheduling method described above.

[0031] This application's embodiments can achieve full-process optimization of energy storage systems through the collaboration of a multi-timescale data system and a scheduling model. It establishes a three-level data system covering day-ahead, intraday, and real-time data, acquiring multi-precision data in a differentiated manner to provide accurate input for the scheduling model. A three-layer scheduling model is designed to form closed-loop control, optimizing cost and response efficiency layer by layer. A layered objective function design balances operating costs and renewable energy absorption. Linearization of nonlinear constraints improves model solution efficiency, thereby enhancing the absorption capacity, operational economy, and stability of high-proportion renewable energy grids. This solves the problems in related technologies, such as the disconnect between pumped storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and lack of reserve capacity coordination mechanisms, leading to low energy storage efficiency, insufficient scheduling refinement, and high system reserve costs.

[0032] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0033] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is a flowchart of a pumped-storage coupled compressed-air energy storage scheduling method according to an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of a pumped-storage hybrid compressed-air energy storage power station system according to an embodiment of this application;

[0036] Figure 3 This is a schematic diagram of a pumped-storage coupled compressed-air energy storage scheduling device according to an embodiment of this application;

[0037] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0038] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0039] The following describes a pumped-storage coupled compressed-air energy storage scheduling method according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues raised in the background section regarding the aforementioned hybrid system scheduling methods, such as the disconnect between pumped-storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and the lack of a backup capacity coordination mechanism, this application provides a pumped-storage coupled compressed-air energy storage scheduling method. This method establishes a three-level data system covering the 24-hour day-ahead period, the 1-hour intraday period, and the 15-minute real-time period. It differentiates and acquires multi-precision data, designs a "day-ahead planning - intraday correction - real-time response" collaborative model, optimizes scheduling strategies layer by layer, balances renewable energy curtailment storage with load demand, leverages the complementary advantages of the two types of energy storage systems, designs a low-valley energy storage and peak-hour energy release coordination mechanism, optimizes backup capacity allocation, and employs mixed-integer linear programming combined with piecewise linearization techniques to handle nonlinear constraints, improving solution efficiency. This, in turn, enhances the absorption capacity, operational economy, and stability of high-proportion renewable energy power grids. This solves the problems in related technologies, such as the disconnect between pumped storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and lack of backup capacity coordination mechanism in hybrid system scheduling methods, which lead to low energy storage efficiency, insufficient scheduling refinement, and high system backup costs.

[0040] Specifically, Figure 1 This is a schematic flowchart of a pumped-storage coupled compressed-air energy storage scheduling method provided in an embodiment of this application.

[0041] like Figure 1 As shown, the pumped hydro storage coupled with compressed air storage scheduling method includes the following steps:

[0042] In step S101, a multi-level data system is established based on the input data at multiple time scales.

[0043] It is understood that the multi-timescale input data in the embodiments of this application can cover day-ahead, intraday and real-time data, and the input data can include, but is not limited to, wind power, photovoltaic output forecast, load forecast, initial water volume of pumped storage upper reservoir and initial gas storage pressure of CAES.

[0044] In actual implementation, the embodiments of this application can construct a system by collecting data at different time scales in stages, such as providing hierarchical input support for three-level scheduling.

[0045] The embodiments of this application can effectively improve the response speed of the energy storage system to the fluctuation of new energy sources through hierarchical data input, reduce the scheduling inaccuracy caused by prediction errors, reduce the curtailment rate of wind and solar power, and enhance the stability and economy of system operation.

[0046] Optionally, in one embodiment of this application, a multi-level data system is established, including: constructing multi-timescale data input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, to obtain coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data respectively; and constructing a three-level multi-level data system based on the coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data.

[0047] In practical implementation, the embodiments of this application can obtain wind power and photovoltaic output forecasts, load forecasts, initial water volume of pumped storage upper reservoirs, and initial gas storage pressure of CAES 24 hours in advance during the day-ahead phase to determine the end-of-day energy balance; update the forecast data 1 hour in advance during the intraday phase, dynamically limit the rate of change of reservoir water volume and gas storage tank pressure, and correct short-term deviations; and obtain ultra-short-term data 15 minutes in advance during the real-time phase to capture instantaneous fluctuations and generate control commands. A three-level data system covering multiple time scales, such as 24 hours in advance, 1 hour in the intraday phase, and 15 minutes in real-time, is established. Considering the characteristic that the accuracy of wind power, photovoltaic, and load forecasts increases with the shortening of time, coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data are obtained differentially. A three-level multi-level data system is constructed based on the coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data.

[0048] In some embodiments, a four-level multi-level data system can be constructed based on input data at multiple time scales, such as 24 hours from the previous day, 12 hours from the current day, 1 hour from the current day, and 15 minutes from the current time. Of course, in other embodiments, different time scales can be further updated to construct a five-level multi-level data system.

[0049] The embodiments of this application can collect prediction data at different time scales. Through a hierarchical and progressive optimization approach, it can more accurately match the output characteristics of new energy sources with the load demand of the power grid, improve the utilization efficiency of energy storage devices and the flexibility of power grid regulation, and provide accurate input for subsequent scheduling models.

[0050] In step S102, a multi-time-scale scheduling model is constructed using a multi-level data system.

[0051] In practical implementation, the embodiments of this application can utilize a multi-level data system to construct a multi-time-scale scheduling model, with each layer achieving scheduling optimization through specific objective functions and constraints. Based on the characteristics of data at different time scales, a multi-layered model architecture is divided, such as a three-layer architecture of "long-cycle planning, medium-cycle correction, and short-cycle response." Each layer matches a corresponding objective function, and combined with conditions such as energy balance and equipment state constraints, scheduling optimization is promoted layer by layer, allowing the model to adapt to the needs of different time periods, dynamically regulating the energy storage system from macro to micro levels, and achieving efficient operation in multiple scenarios.

[0052] The embodiments of this application can realize hierarchical scheduling of long, medium and short cycles in the time dimension, enabling the model to accurately match the scheduling needs of different time periods. By promoting scheduling optimization in a hierarchical manner, deep synergy between pumped storage and CAES is realized, which significantly improves the precision of energy storage system scheduling, reduces system operating costs, and improves the utilization efficiency of energy storage equipment and grid regulation capabilities.

[0053] Optionally, in one embodiment of this application, a multi-timescale scheduling model is constructed using a multi-level data system, including: designing a three-layer model based on a three-level multi-level data system, which consists of day-ahead planning, intraday correction, and real-time response, to formulate a basic plan with the goal of minimizing the total cost throughout the day, adjust the output allocation and limit the rate of state change based on hourly forecasts during the day, and generate control commands in real time using 15-minute ultra-short-term data, thus forming a closed-loop control system.

[0054] In practical implementation, the embodiments of this application can design a collaborative model of day-ahead planning, intraday correction, and real-time response based on a three-level multi-level data system, optimizing the scheduling strategy layer by layer. Day-ahead scheduling aims to minimize the total cost throughout the day, combining renewable energy output forecasts and load curves to formulate basic plans for pumped storage pumping / generation and CAES compression / expansion, simultaneously balancing renewable energy curtailment storage with load demand; intraday scheduling is based on hourly high-precision forecasts, dynamically adjusting energy storage output allocation, limiting the rate of state change, and reducing short-term fluctuation costs due to short-term forecast deviations; real-time scheduling utilizes 15-minute ultra-short-term data to trigger rapid power regulation of pumped storage and short-term energy release mechanisms of CAES, filling the renewable energy output gap in real time, ultimately forming a closed-loop control system of "global planning - local correction - instantaneous response," achieving collaborative optimization of energy storage resources across multiple time scales.

[0055] The embodiments of this application can more accurately match the output characteristics of new energy sources with the grid load demand through a hierarchical and progressive optimization approach. This effectively reduces the operating cost of the energy storage system, improves the utilization efficiency of energy storage devices and the flexibility of grid regulation, and enhances the system's ability to cope with fluctuations in new energy sources.

[0056] Optionally, in one embodiment of this application, the objective function of the three-layer model includes:

[0057] minf day-ahead =C PS +C CAES +C penalty ,

[0058] minf intraday =C PS-adj +C CAES-adj +C penalty ,

[0059] minfreal-time =C fast-adjust +C penalty-real ,

[0060] Where, minf day-ahead The objective function is C. PS For pumped storage operation costs, C CAES For CAES operating costs, C penalty The cost of curtailing wind and solar power; minf intraday The objective function for the day is C. PS-adj To adjust the cost of pumped storage, C CAES-adj Adjust costs for CAES output; minf real-time It is a real-time objective function, C fast-adjust To allow for real-time and rapid cost adjustments.

[0061] Specifically, the objective function for day-ahead scheduling (24-hour period, 1-hour resolution) is:

[0062] min f day-ahead =C PS +C CAES +C penalty ,

[0063] Among them, C PS The operating cost of pumped storage includes pumping energy consumption costs, power generation revenue, and start-up and shutdown losses; C CAES C represents the operating cost of CAES, including compression energy consumption costs and expansion power generation revenue. penalty The cost of penalties for abandoning wind and solar power.

[0064] In some embodiments, the key constraints for day-ahead scheduling include dynamic balance of pumped storage water volume, mutual exclusion of CAES operating states, and system power balance.

[0065] The dynamic equilibrium is as follows:

[0066]

[0067] The water level boundary is limited to:

[0068] X min ≤X t ≤X max ,

[0069] The daily water balance is:

[0070] X T =X0,

[0071] Among them, X t The water volume in the reservoir during time period t. Pumping efficiency (m) 3 / (MW·h)), Power generation efficiency (MW·h / m) 3 ), Pumping power, X represents the power generation capacity. min / X max This represents the minimum / maximum water storage capacity of the reservoir.

[0072] The CAES runtime states are mutually exclusive:

[0073] u com,t +u exp,t ≤1,

[0074] Among them, u com,t =1 indicates that CAES is in compression mode during time period t, u exp,t =1 indicates that it is in expansion mode, and the two cannot be activated at the same time.

[0075] The system power balance is as follows:

[0076]

[0077] Among them, P w,t / P v,t For wind / solar power output, P L,t For load, P com,t / P exp,t This refers to the CAES compression / expansion power.

[0078] The recent dispatch output is the pumped storage hydropower / hydropower generation plan for the next 24 hours. CAES compression / expansion program (u com,t ,u exp,t ,P com,t ,P exp,t ).

[0079] In some embodiments, the objective function for intraday scheduling (1-hour period, 15-minute resolution) is:

[0080] min f intraday =C PS-adj +C CAES-adj +C penalty ,

[0081] Among them, C PS-adj / C CAES-adj Adjusting costs for pumped storage and CAES output.

[0082] Specifically, key constraints for intraday scheduling include limits on the rate of change of water volume, limits on the rate of change of CAES pressure, and constraints on the ramp-up of thermal power units, resulting in an output adjustment plan for the next hour.

[0083] The rate of change of water volume is limited as follows:

[0084] |X t -X t-1 |≤ΔX max ,

[0085] The CAES pressure change rate limit is:

[0086] |P t -P t-1 |≤ΔP max ,

[0087] The ramping constraints for thermal power units (if any) are as follows:

[0088]

[0089] in, This represents the ramp-up / ramp rate of the thermal power unit, expressed in MW / h. The output results are the planned output adjustments for pumped storage and CAES for the next hour, including power fine-tuning and mode switching timing.

[0090] Specifically, the objective function for real-time scheduling is:

[0091] minf real-time =C fast-adjust +C penalty-real ,

[0092] Among them, C fast-adjust To allow for real-time and rapid cost adjustments, this priority is higher than intraday cost adjustments.

[0093] In some cases, key constraints for real-time scheduling include pumped storage response time and minimum continuous operating time of CAES, outputting real-time adjustment instructions for the next 15 minutes and emergency backup start-up signals.

[0094] The pumped storage response time is:

[0095]

[0096] The minimum continuous runtime of CAES is:

[0097]

[0098] Where N=1, that is, a 15-minute period.

[0099] The output of real-time scheduling is the real-time adjustment instructions for pumped storage and CAES for the next 15 minutes, including power correction and emergency backup start signals.

[0100] The embodiments of this application can design a collaborative model of "day-to-day planning - intraday correction - real-time response", optimize the scheduling strategy layer by layer, and form a closed-loop control system of "global planning - local correction - instantaneous response".

[0101] In step S103, based on the multi-time-scale scheduling model, when the load of the pumped-storage hybrid compressed-air energy storage power station is lower than the first preset threshold, priority is given to power curtailment and pumping and compression are carried out. When the load is greater than the second preset threshold, the pumped-storage hybrid compressed-air energy storage power station is controlled to release energy in combination. The nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure is transformed into linear constraints. The solution is obtained by dividing the water level range and pressure level to obtain the pumped-storage coupled compressed-air energy storage scheduling strategy.

[0102] In one example of this invention, the first preset threshold can be a load trough, and the second preset threshold can be a load peak. It should be noted that the setting of the first and second preset thresholds is merely for illustrative purposes and is not intended to limit the scope. Due to the complexity of actual power grid operation scenarios, it is necessary to comprehensively consider factors such as the output characteristics of new energy sources, the capacity of energy storage devices, and the requirements for power grid security and stability. The load curve can be referenced, with the trough threshold corresponding to the nighttime low-load threshold, and the peak threshold determined by combining morning and evening peak times and the energy release critical point.

[0103] In actual implementation, the embodiments of this application can design a two-layer collaborative strategy to achieve efficient energy conversion and intelligent allocation of reserve capacity, based on the core characteristics of rapid response of pumped hydro storage and large-capacity long-cycle storage of CAES, thereby breaking the application limitations of a single energy storage system.

[0104] Specifically, during periods of low load and excess renewable energy (such as nighttime wind power generation), priority is given to utilizing abandoned power to drive pumped-storage hydroelectric pumping (increasing reservoir water volume) and CAES compression (increasing gas storage tank pressure). Energy storage power is dynamically allocated according to the "remaining capacity ratio" to maximize the absorption of abandoned power. For example, if the reservoir's remaining capacity > CAES capacity, more pumping power is allocated to reduce abandoned power losses. During peak load periods or when renewable energy output is insufficient, pumped-storage hydroelectric power rapidly generates electricity to cope with high-frequency fluctuations (response time ≤ 2 minutes), while CAES continuously releases energy to meet medium- and long-term needs (a single unit can sustain for more than 8 hours). The two work together, with pumped-storage hydroelectric power handling the first 30 minutes of instantaneous regulation and CAES providing stable power output, reducing the number of start-ups and shutdowns of thermal power units.

[0105] Furthermore, in terms of backup capacity allocation, pumped storage provides rapid backup (≤5-minute response), while CAES provides continuous backup (≥30-minute support). Pumped storage reserves 10%-15% of its rated power as rapid backup to address instantaneous deviations in wind / solar power and ensure system frequency fluctuations ≤±0.2Hz. CAES reserves 20% of its rated expanded power as continuous backup to compensate for accumulated intraday forecast errors, jointly reducing system backup requirements and decreasing backup pressure on thermal power units.

[0106] Furthermore, by employing mixed-integer linear programming combined with piecewise linearization techniques, the nonlinear relationship between pumped storage efficiency and water level, and between compressed air energy storage power and pressure, is transformed into linear constraints. This allows for efficient solutions by dividing the water level range and pressure level.

[0107] This application's embodiments leverage the complementary advantages of pumped storage and CAES (Cyclic Energy Storage System) to achieve efficient energy conversion and intelligent allocation of reserve capacity. Through nonlinear constraint linearization, model solution efficiency is improved, system reserve costs are reduced, the number of thermal power unit start-ups and shutdowns is decreased, and the grid's ability to absorb new energy sources is effectively enhanced.

[0108] Optionally, in one embodiment of this application, the nonlinear relationship between pumped storage efficiency and water level, and between compressed air storage power and pressure, is transformed into linear constraints, which are then solved by dividing water level intervals and pressure levels. This includes: in the process of solving the nonlinear constraints, dividing the water volume of the pumped storage reservoir into multiple water level intervals, wherein the relationship between power generation efficiency and water volume is approximated by a linear equation within each water level interval; defining the upper limit of expansion power according to the pressure level of the gas storage tank, and establishing a linear mapping between the pressure level and the upper limit of power for solving.

[0109] It is understood that the nonlinear constraint solution in the embodiments of this application can be the pumped storage efficiency changing with water level, and the coupling of CAES power and pressure.

[0110] In practical implementation, the embodiments of this application can address the nonlinear constraints in the coordinated scheduling of pumped storage and compressed air energy storage, such as the change in pumped storage efficiency with water level and the coupling of CAES power with gas tank pressure. By using mixed integer linear programming combined with piecewise linearization technology, complex nonlinear relationships are transformed into a combination of linear constraints, thereby achieving efficient solution of the model and engineering implementation.

[0111] Specifically, the water volume of pumped storage reservoirs is divided into three intervals: low, medium, and high. Within each interval, the relationship between power generation efficiency and water volume is approximated by a linear equation. At the same time, the upper limit of expansion power is defined according to the pressure level of the CAES gas storage tank. A linear mapping between pressure level and power upper limit is established through the Big M method, avoiding the direct solution of complex thermodynamic equations and significantly improving the solution efficiency.

[0112] The embodiments of this application can improve the solution efficiency of the scheduling model, shorten the calculation time, enable the scheduling strategy to be generated and executed faster, provide reliable technical support for the optimized scheduling of energy storage systems, and reduce the computational cost of system operation.

[0113] Specifically, it can be combined with Figure 2 As shown, the working principle of the pumped-storage coupled compressed-air energy storage scheduling method in this application is explained in detail with a specific embodiment.

[0114] like Figure 2 As shown, the embodiments of this application may include: a reservoir connected to a water-air co-containment chamber, where water can circulate through pipelines and compressed air is also stored in the chamber, providing a material basis for subsequent pumped water storage and compressed air energy storage; three sets of compressors connected in series with heat exchangers, where the heat generated when the compressors are electrically driven to compress air is transferred to a high-temperature heat storage tank via the heat exchangers; and three sets of expanders connected in series with heat exchangers, where the heat from the high-temperature heat storage tank is released via the heat exchangers when the air expands and drives the expanders to generate electricity, maintaining the efficiency of the expansion process, and storing the low-temperature waste heat in a low-temperature heat storage tank.

[0115] During off-peak hours, electricity drives the compressor to compress air, store water, and store heat in a high-temperature tank. During peak hours, air expands and water is released, driving an expander to generate electricity. The heat from the thermal storage tank is used to improve power generation efficiency, achieving energy conversion and storage scheduling. The system achieves energy conversion through the circulation and storage of water, air, and heat. Combined with a multi-timescale scheduling model, it forms a closed-loop control system, from day-ahead global energy storage planning to intraday equipment status correction and real-time response, overcoming the limitations of single energy storage and improving the absorption of new energy sources and grid stability.

[0116] The pumped-storage coupled compressed-air storage (CAES) energy storage scheduling method proposed in this application establishes a three-level data system covering 24 hours day-ahead, 1 hour intraday, and 15 minutes real-time. It acquires multi-precision data in a differentiated manner, designs a collaborative model of "day-ahead planning - intraday correction - real-time response," optimizes scheduling strategies layer by layer, balances renewable energy curtailment storage with load demand, leverages the complementary advantages of the two types of energy storage systems, designs a collaborative mechanism for off-peak energy storage and peak energy release, optimizes the allocation of reserve capacity, and employs mixed-integer linear programming combined with piecewise linearization techniques to handle nonlinear constraints, improving solution efficiency. This enhances the absorption capacity, operational economy, and stability of high-proportion renewable energy power grids. Therefore, it solves the problems in related technologies, such as the disconnect between pumped-storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and the lack of a reserve capacity collaborative mechanism, which lead to low energy storage efficiency, insufficient scheduling refinement, and high system reserve costs.

[0117] Next, referring to the accompanying drawings, a pumped-storage coupled compressed-air energy storage scheduling device according to an embodiment of this application is described.

[0118] Figure 3 This is a schematic diagram of the structure of the pumped-storage coupled compressed-air energy storage scheduling device according to an embodiment of this application.

[0119] like Figure 3 As shown, the pumped-storage coupled compressed-air energy storage scheduling device 10 includes: a setup module 100, a construction module 200, and a scheduling module 300.

[0120] Specifically, module 100 is established to build a multi-level data system based on input data at multiple time scales.

[0121] Module 200 is used to build a multi-time-scale scheduling model using a multi-level data system.

[0122] The scheduling module 300 is used to prioritize power curtailment and pumping and compression when the load of the pumped-storage hybrid compressed-air energy storage power station is lower than the first preset threshold, based on a multi-time-scale scheduling model. When the load is greater than the second preset threshold, the module controls the pumped-storage hybrid compressed-air energy storage power station to release energy in combination. The nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure, is transformed into a linear constraint. The constraint is solved by dividing the water level range and pressure level to obtain the pumped-storage coupled compressed-air energy storage scheduling strategy.

[0123] Optionally, in one embodiment of this application, the establishment module 100 includes: a data acquisition unit and a system construction unit.

[0124] The data acquisition unit is used to construct multi-timescale data input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, in order to acquire coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data, respectively.

[0125] The system construction unit is used to build a three-level multi-level data system based on coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data.

[0126] Optionally, in one embodiment of this application, the construction module 200 includes a control unit.

[0127] The control unit is used to design a three-layer model based on a three-level multi-level data system, which includes day-ahead planning, intraday correction, and real-time response. This model utilizes day-ahead planning with the goal of minimizing the total daily cost, intraday adjustments to output allocation based on hourly forecasts and limits the rate of state change, and real-time generation of control commands using 15-minute ultra-short-term data to form a closed-loop control system.

[0128] Optionally, in one embodiment of this application, the objective function of the three-layer model includes:

[0129] min fday-ahead =C PS +C CAES +C penalty ,

[0130] min f intraday =C PS-adj +C CAES-adj +C penalty ,

[0131] minf real-time =C fast-adjust +C penalty-real

[0132] Where, min f day-ahead The objective function is C. PS For pumped storage operation costs, C CAES For CAES operating costs, C penalty The cost of curtailing wind and solar power; min f intraday The objective function for the day is C. PS-adj To adjust the cost of pumped storage, C CAES-adj Adjust costs for CAES output; minf real-time It is a real-time objective function, C fast-adjust To allow for real-time and rapid cost adjustments.

[0133] Optionally, in one embodiment of this application, the scheduling module 300 includes a partitioning unit and a mapping unit.

[0134] The division unit is used to divide the water volume of the pumped storage reservoir into multiple water level intervals during the nonlinear constraint solution process. Within each water level interval, the relationship between power generation efficiency and water volume is approximated by a linear equation.

[0135] The mapping unit is used to define the upper limit of expansion power according to the pressure level of the gas storage tank, and to establish a linear mapping between the pressure level and the upper limit of power for solution.

[0136] It should be noted that the foregoing explanation of the pumped storage coupled compressed air energy storage scheduling method also applies to the pumped storage coupled compressed air energy storage scheduling device of this embodiment, and will not be repeated here.

[0137] The pumped-storage coupled compressed-air energy storage scheduling device proposed in this application can establish a three-level data system covering 24 hours day-ahead, 1 hour intraday, and 15 minutes real-time. It can acquire multi-precision data in a differentiated manner, design a collaborative model of "day-ahead planning - intraday correction - real-time response," and optimize scheduling strategies layer by layer. This balances the storage of abandoned renewable energy with load demand. Leveraging the complementary advantages of the two types of energy storage systems, it designs a collaborative mechanism for off-peak energy storage and peak energy release, optimizes the allocation of reserve capacity, and employs mixed-integer linear programming combined with piecewise linearization technology to handle nonlinear constraints, improving solution efficiency. This enhances the absorption capacity, operational economy, and stability of high-proportion renewable energy grids. Therefore, it solves the problems in related technologies, such as the separation of pumped-storage water volume scheduling and CAES energy management, insufficient utilization of multi-timescale prediction accuracy, and the lack of a reserve capacity collaborative mechanism, which lead to low energy storage efficiency, insufficient scheduling refinement, and high system reserve costs.

[0138] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0139] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0140] When the processor 402 executes the program, it implements the pumped storage coupled compressed air energy storage scheduling method provided in the above embodiments.

[0141] Furthermore, electronic devices also include:

[0142] Communication interface 403 is used for communication between memory 401 and processor 402.

[0143] The memory 401 is used to store computer programs that can run on the processor 402.

[0144] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0145] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0146] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0147] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0148] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the pumped-storage coupled compressed-air energy storage scheduling method described above.

[0149] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described pumped-storage coupled compressed-air energy storage scheduling method.

[0150] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0151] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0152] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0154] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0155] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0156] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for scheduling pumped hydro storage coupled with compressed air energy storage, characterized in that, Includes the following steps: A multi-level data system is established based on input data at multiple time scales; A multi-time-scale scheduling model is constructed using the aforementioned multi-level data system; Based on the multi-timescale scheduling model, when the load of the pumped-storage hybrid compressed-air energy storage power station is lower than the first preset threshold, priority is given to power curtailment and pumping and compression are carried out. When the load is greater than the second preset threshold, the pumped-storage hybrid compressed-air energy storage power station is controlled to release energy in combination. The nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure, is transformed into a linear constraint. The solution is obtained by dividing the water level range and pressure level to obtain the pumped-storage coupled compressed-air energy storage scheduling strategy.

2. The method according to claim 1, characterized in that, The establishment of a multi-level data system includes: Construct multi-timescale data input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, to obtain coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data respectively; The three-level multi-level data system is constructed based on the coarse-grained planning data, the high-precision correction data, and the ultra-short-term real-time data.

3. The method according to claim 2, characterized in that, The construction of a multi-time-scale scheduling model using the multi-level data system includes: Based on the aforementioned three-level multi-level data system, a three-layer model of day-ahead planning, intraday correction, and real-time response is designed. This model utilizes day-ahead planning with the goal of minimizing the total daily cost, intraday adjustments to output allocation based on hourly forecasts and limiting the rate of state change, and real-time generation of control commands using 15-minute ultra-short-term data to form a closed-loop control system.

4. The method according to claim 3, characterized in that, The objective function of the three-layer model includes: minf day-ahead =C PS +C CAES +C penalty , minf intraday =C PS-adj +C CAES-adj +C penalty , minf real-time =C fast-adjust +C penalty-real , Where, minf day-ahead The objective function is C. PS For pumped storage operation costs, C CAES For CAES operating costs, C penalty The cost of curtailing wind and solar power; minf intraday The objective function for the day is C. PS-adj To adjust the cost of pumped storage, C CAES-adj Adjust costs for CAES output; minf real-time It is a real-time objective function, C fast-adjust To allow for real-time and rapid cost adjustments.

5. The method according to claim 1, characterized in that, The process transforms the nonlinear relationships between pumped storage efficiency and water level, and between compressed air storage power and pressure, into linear constraints. These constraints are then solved by dividing the water level range and pressure levels. This includes: In the process of solving the nonlinear constraints, the water volume of the pumped storage reservoir is divided into multiple water level intervals. In each water level interval, the relationship between power generation efficiency and water volume is approximated by a linear equation. The upper limit of expansion power is defined according to the pressure level of the gas storage tank, and a linear mapping between the pressure level and the upper limit of power is established for solution.

6. A pumped-storage coupled compressed-air energy storage scheduling device, characterized in that, include: Establish a module to build a multi-level data system based on input data at multiple time scales; The construction module is used to build a multi-time-scale scheduling model using the multi-level data system; The scheduling module is used to prioritize power abandonment and pumping and compression when the load of the pumped-storage hybrid compressed-air energy storage power station is lower than a first preset threshold, based on the multi-time-scale scheduling model. When the load is greater than a second preset threshold, the module controls the pumped-storage hybrid compressed-air energy storage power station to release energy in combination. The nonlinear relationship between pumped-storage efficiency and water level, and between compressed-air energy storage power and pressure, is transformed into a linear constraint. The constraint is solved by dividing the water level range and pressure level to obtain the pumped-storage coupled compressed-air energy storage scheduling strategy.

7. The apparatus according to claim 6, characterized in that, The establishment module includes: The data acquisition unit is used to construct multi-timescale data input data covering the previous 24 hours, the intraday 1 hour, and the real-time 15 minutes, so as to acquire coarse-grained planning data, high-precision correction data, and ultra-short-term real-time data respectively. The system construction unit is used to construct the three-level multi-level data system based on the coarse-grained planning data, the high-precision correction data, and the ultra-short-term real-time data.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pumped-storage coupled compressed-air energy storage scheduling method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the pumped-storage coupled compressed-air energy storage scheduling method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the pumped-storage coupled compressed-air energy storage scheduling method as described in any one of claims 1-5.

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