An economic dispatch method for compressed air energy storage power station fusing uncertainty information

Abstract

The application discloses a compressed air energy storage power station economic dispatching method fusing uncertainty information. The application comprises the following steps: collecting multi-source data such as new energy output, load, electricity price and equipment state and uniformly pre-processing time scales. Various types of operation uncertainties are identified, a typical dispatching scenario set is constructed through probability and interval representation, a dispatching optimization model with the optimal operation economy as the target is established relying on the scenario set, full-link equipment operation constraints are introduced and a dispatching scheme is solved. The safety and feasibility of the dispatching scheme are checked, and various types of unit and valve control instructions are issued after the check is passed; real-time power station operation data are collected, and the dispatching scheme is rolled over and corrected according to the feedback. The application fully considers the influence of multiple random disturbances, takes into account the equipment physical operation boundary constraints, improves the landing performance of the scheme through rolling closed-loop dispatching, and improves the operation economic benefits of the compressed air energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of compressed air energy storage power station operation control and power system economic dispatch technology, and in particular to an economic dispatch method for compressed air energy storage power stations that integrates uncertainty information. Background Technology

[0002] As the installed capacity of new energy sources such as wind power and photovoltaics continues to increase, the power output volatility and forecasting uncertainty in the power system are gradually increasing. New energy power generation is characterized by randomness, intermittency, and volatility, and its actual output often deviates from the day-ahead or intraday forecast results. Simultaneously, user-side load demand is also affected by factors such as weather conditions, electricity consumption behavior, and changes in production plans, resulting in fluctuations. These uncertainties directly impact the power system's power balance, peak shaving and frequency regulation needs, and the operational profitability of energy storage power stations.

[0003] Compressed air energy storage power stations are a type of energy storage suitable for large-capacity, long-term energy storage scenarios. They typically include a compressor unit, a heat storage / exchange device, an air storage device, an expander, a generator, and a grid / load interface. When electricity prices are low or renewable energy sources are abundant, the power station drives the compressor unit to compress air and store it in the air storage device. When electricity prices are high or the system needs power, the air storage device releases the compressed air, which is then used by the expander to drive the generator to produce electricity. Due to its advantages such as large energy storage capacity, long operating life, and suitability for grid peak shaving and renewable energy integration, compressed air energy storage power stations have high application value in new power systems.

[0004] Existing economic dispatch methods for compressed air energy storage power plants are mostly based on deterministic forecasts. This means that parameters such as renewable energy output, load demand, and electricity prices are typically treated as fixed inputs, and a dispatch model that minimizes operating costs or maximizes profits is then established. While this type of method can achieve certain results when forecast errors are small or operating conditions are stable, significant fluctuations in renewable energy output, load demand, time-of-use electricity prices, and equipment operating status can lead to deviations in dispatch schemes based on single forecast values. This can result in problems such as unreasonable compressor start-up and shutdown, gas storage pressure deviating from the target range, difficulty in executing expander output plans, and increased fluctuations in grid-connected power.

[0005] Furthermore, compressed air energy storage power stations differ from conventional electrochemical energy storage systems in that they involve coupling relationships between compression, gas storage, heat exchange, and expansion power generation processes. Parameters such as compressor unit power, gas storage device pressure, charging flow rate, releasing flow rate, heat storage / heat exchange status, expander output, and generator power interact with each other. If the scheduling model optimizes only from the perspective of electrical power without fully considering the equipment operation constraints and internal state constraints of the compressed air energy storage power station, the scheduling results may be mathematically feasible but difficult to directly translate into practically executable equipment control commands.

[0006] Existing technologies also include research or methods that incorporate uncertainties into energy storage scheduling, such as handling new energy and load forecasting errors through scenario analysis, robust optimization, or chance constraints. However, when applied to compressed air energy storage power plants, these methods often have the following shortcomings: First, they do not comprehensively consider the sources of uncertainty information, typically focusing only on new energy output or load forecasting errors, while rarely integrating multiple types of information such as time-of-use pricing, equipment status, gas storage pressure, flow rate, temperature, and power feedback. Second, they do not adequately consider the physical operating characteristics of compressed air energy storage power plants, failing to fully reflect the operational coupling relationship between compressor units, thermal storage / heat exchange devices, gas storage devices, release regulating valves, expanders, and generators. Third, scheduling schemes often remain at the day-ahead or static optimization level, lacking a feedback correction mechanism based on real-time operating status, making it difficult to respond promptly to forecast deviations and changes in equipment status. Fourth, there is a lack of clear correspondence between scheduling results and specific execution objects, making it difficult to directly issue executable commands such as compressor unit start / stop, gas charging valve opening, release regulating valve opening, expander output, and generator grid-connected power.

[0007] Therefore, there is an urgent need to propose an economic dispatch method for compressed air energy storage power stations that integrates uncertain information. This method would incorporate multi-source information, such as renewable energy output, load demand, time-of-use pricing, equipment operating status, and historical operating data, into the dispatch process. Through uncertainty identification, error statistics, scenario generation, constraint construction, optimization solutions, and real-time feedback correction, a dispatch scheme for compressed air energy storage power stations that balances economy, safety, and feasibility can be formed. This would improve the operational economy and dispatch reliability of compressed air energy storage power stations under renewable energy fluctuations. Summary of the Invention

[0008] The present invention aims to at least partially solve one of the technical problems in the related art.

[0009] Therefore, the first objective of this invention is to propose an economic dispatch method for compressed air energy storage power plants that incorporates uncertain information.

[0010] Another objective of this invention is to propose an economic dispatch device for compressed air energy storage power stations that integrates uncertain information.

[0011] The third objective of this invention is to provide a computer device.

[0012] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0013] To achieve the above objectives, a first aspect of the present invention proposes an economic dispatch method for compressed air energy storage power stations that integrates uncertainty information, comprising: S10, acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. Perform data preprocessing on the multi-source input data to obtain scheduling input data that meets a unified time scale. S20, based on the scheduling input data, identify the uncertainty information affecting the economic scheduling of the compressed air energy storage power station. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. Then, perform error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a typical scheduling scenario set. S30, the typical scheduling scenario set is input into the economic scheduling model construction unit. Combining time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters, and risk control parameters, an economic scheduling optimization model for compressed air energy storage power plants is established. The economic scheduling optimization model includes an objective function with optimal operating economy as the goal, as well as constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints, and grid interface power constraints. The economic scheduling optimization model is then optimized and solved to obtain an economic scheduling scheme. S40, the safety and executability of the economic dispatch scheme are checked. If the requirements are met, an economic dispatch instruction is output and sent to the compressed air energy storage power station execution object. The economic dispatch instruction includes at least one of the following: compressor unit start / stop instruction, air charging regulating valve opening instruction, air release regulating valve opening instruction, expander output instruction, generator grid connection power instruction, and grid interface power allocation instruction. At the same time, the operating status data of the compressed air energy storage power station is collected, and the economic dispatch scheme is rolled over and corrected according to the operating status feedback results.

[0014] In one embodiment of the present invention, S10 includes: Perform data cleaning on the multi-source input data to remove erroneous or duplicate data; Outliers were removed from data that exceeded the reasonable operating range of the equipment. Missing data were filled in by interpolation between adjacent times, filling in data on similar historical days, or filling in data using predictive models. The filled data was then normalized to eliminate differences in different units. Furthermore, the normalized data is standardized in terms of time scale, aligning new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information, and historical operating data to the same scheduling cycle, thereby obtaining scheduling input data that meets the unified time scale.

[0015] In one embodiment of the present invention, S20 includes: Historical forecasts and actual operating values ​​are input into the uncertainty information analysis unit for comparison to construct a forecast error sequence; Based on the prediction error sequence, identify the sources of uncertainty and determine whether the sample data meets the requirements of probability modeling; When the number of historical samples is sufficient and the error distribution has a statistical regularity, the uncertainty information is characterized by a probability distribution. When the number of samples is insufficient or the error distribution pattern is not obvious, the uncertainty information is characterized by the interval boundary method. An initial set of uncertain scenarios is constructed based on the probability distribution characterization results or interval boundary characterization results, and the scenario probability or confidence weight of each uncertain scenario is calculated. The initial set of uncertain scenarios is filtered and reduced to retain a set of typical scheduling scenarios that can represent the main fluctuation characteristics.

[0016] In one embodiment of the present invention, S40 includes: Check whether the pressure of the gas storage device is within the allowable range, whether the power of the compressor unit exceeds the rated limit, whether the opening of the gas charging regulating valve and the gas releasing regulating valve meets the regulation range, whether the output of the expander meets the operating boundary, whether the power of the generator meets the grid connection requirements, and whether the power of the grid interface meets the grid dispatch or load power supply requirements. When the economic scheduling scheme meets the requirements of safety and executability, an economic scheduling instruction is output; When the economic scheduling scheme does not meet the requirements of safety and feasibility, the model parameters, risk parameters or constraints are modified, and the optimization solution is re-run until the economic scheduling scheme that meets the requirements is obtained.

[0017] In one embodiment of the present invention, it further includes: A multi-timescale rolling scheduling mechanism combining day-ahead, intraday, and real-time scheduling is adopted. In the day-ahead scheduling phase, a day-ahead baseline scheduling plan is formed based on day-ahead forecast data and day-ahead uncertainty scenario set. In the intraday correction phase, the day-ahead baseline scheduling plan is corrected based on intraday updated data and forecast deviation to form an intraday corrected scheduling plan. During the real-time rolling phase, the intraday revised scheduling plan is rolled out based on real-time operating data and execution deviations to form real-time scheduling correction instructions, which are then sent to the compressed air energy storage power station execution objects.

[0018] In one embodiment of the present invention, it further includes: The operational status data is collected by multiple monitoring points installed in the compressed air energy storage power station; The monitoring points include a gas flow monitoring point for collecting the gas flow rate during the energy storage stage, a gas temperature monitoring point for collecting the gas temperature during the energy storage stage, a gas temperature monitoring point for collecting the gas temperature during the energy release stage, a pressure monitoring point for collecting the pressure status of the gas storage device, a gas flow monitoring point for collecting the gas flow rate during the energy release stage, an equipment status monitoring point for collecting the start-stop status, operating status, or fault status of the compressor unit, gas regulating valve, gas releasing regulating valve, expander, and generator, and a power monitoring point for collecting power data at the generator or grid interface. The operational status data collected by the multiple monitoring points are incorporated into the operational status monitoring and feedback unit. The operational status monitoring and feedback unit calculates the deviation between the actual operational status and the economic scheduling scheme. When the deviation does not exceed the set threshold, the current scheduling scheme is maintained and continues to be executed. When the deviation exceeds the set threshold, correction parameters are generated and uncertainty information, model parameters or constraints are updated. The solution is then re-optimized and the correction scheduling instruction is output.

[0019] In one embodiment of the present invention, it further includes: The compressed air energy storage power station includes compressor units, heat storage / exchange devices, gas storage devices, expanders, generators, and grid interfaces; Among them, the compressor unit is used to consume electrical energy and compress air during the energy storage stage, the heat storage or heat exchange device is used to recover, transfer or regulate the heat generated during the compression process, and the air storage device is used to store compressed air. An expander is used to expand compressed air to do work during the energy release phase. A generator is used to convert the mechanical energy output by the expander into electrical energy. A grid interface is used to realize the grid connection of electrical energy or to supply power to the load.

[0020] To achieve the above objectives, a second aspect of the present invention provides an economic dispatch device for a compressed air energy storage power station that integrates uncertainty information, comprising: The multi-source data preprocessing module is used to acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. The module preprocesses the multi-source input data to obtain scheduling input data that meets a unified time scale. An uncertainty scenario construction module is used to identify uncertainty information affecting the economic dispatch of compressed air energy storage power stations based on the dispatch input data. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. The module performs error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a set of typical dispatch scenarios. The economic dispatch optimization solution module is used to input the typical dispatch scenario set into the economic dispatch model construction unit, and combine time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters and risk control parameters to establish an economic dispatch optimization model for compressed air energy storage power plants. The economic dispatch optimization model includes an objective function with the goal of optimizing operating economy and constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints and grid interface power constraints. The module optimizes and solves the economic dispatch optimization model to obtain an economic dispatch scheme. The scheme verification and rolling execution module is used to verify the safety and executability of the economic dispatch scheme. If the requirements are met, it outputs economic dispatch instructions and sends them to the compressed air energy storage power station execution objects. The economic dispatch instructions include at least one of the following: compressor unit start / stop instructions, air charging regulating valve opening instructions, air releasing regulating valve opening instructions, expander output instructions, generator grid connection power instructions, and grid interface power allocation instructions. At the same time, it collects the operating status data of the compressed air energy storage power station and makes rolling corrections to the economic dispatch scheme based on the operating status feedback results.

[0021] The present invention provides an economic dispatch method and apparatus for compressed air energy storage power stations that integrates uncertain information. This method can effectively integrate multi-source uncertain information and improve the operational economy and dispatch reliability of compressed air energy storage power stations in the context of new energy fluctuations.

[0022] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing the method described in the first aspect embodiment.

[0023] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0024] Additional aspects and advantages of the invention 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 the invention. Attached Figure Description

[0025] Figure 1 This is a flowchart of an economic dispatch method for a compressed air energy storage power station that incorporates uncertainty information according to an embodiment of the present invention; Figure 2 This is an overall system block diagram according to an embodiment of the present invention; Figure 3This is a schematic diagram of the relationship between compressed air energy storage power station equipment and scheduling execution according to an embodiment of the present invention; Figure 4 This is a schematic diagram of multi-source uncertainty information acquisition and fusion according to an embodiment of the present invention; Figure 5 This is a schematic diagram of an economic dispatch method for compressed air energy storage power stations that incorporates uncertainty information according to an embodiment of the present invention; Figure 6 This is a flowchart of uncertainty information representation and scheduling scenario generation according to an embodiment of the present invention; Figure 7 This is a logical diagram of the economic scheduling optimization model construction according to an embodiment of the present invention; Figure 8 This is a diagram of the day-ahead, intraday, and real-time rolling economic scheduling mechanism according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the closed loop of scheduling execution and real-time feedback correction according to an embodiment of the present invention; Figure 10 This is a structural diagram of an economic dispatch analysis device for a compressed air energy storage power station that integrates uncertainty information according to an embodiment of the present invention; Figure 11 It is a computer device according to an embodiment of the present invention.

[0026] Among them, 1-1: compressor unit, 1-2: heat storage / heat exchange device, 1-3: gas storage device, 1-4: expander, 1-5: generator, 1-6: power grid / load interface; 2-1: compressor unit, 2-2: gas charging regulating valve, 2-3: heat storage / heat exchange device, 2-4: gas storage device, 2-5: gas release regulating valve, 2-6: expander, 2-7: generator, 2-8: power grid / load interface, 2-9: gas charging flow monitoring point, 2-10: gas charging temperature monitoring point, 2-11: energy release temperature monitoring point, 2-12: pressure monitoring point, 2-13: gas release flow monitoring point, 2-14: equipment status monitoring point, 2-15: power monitoring point. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] The following describes, with reference to the accompanying drawings, a method and apparatus for analyzing the flow rate, efficiency, and water consumption rate of a hydropower plant generator unit according to an embodiment of the present invention.

[0030] Figure 1 This is a flowchart of an economic dispatch method for a compressed air energy storage power station that incorporates uncertainty information according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S10, acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. Perform data preprocessing on the multi-source input data to obtain scheduling input data that meets a unified time scale. S20, based on the scheduling input data, identify the uncertainty information affecting the economic scheduling of the compressed air energy storage power station. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. Then, perform error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a typical scheduling scenario set. S30, the typical scheduling scenario set is input into the economic scheduling model construction unit. Combining time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters, and risk control parameters, an economic scheduling optimization model for compressed air energy storage power plants is established. The economic scheduling optimization model includes an objective function with optimal operating economy as the goal, as well as constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints, and grid interface power constraints. The economic scheduling optimization model is then optimized and solved to obtain an economic scheduling scheme. S40, the safety and executability of the economic dispatch scheme are checked. If the requirements are met, an economic dispatch instruction is output and sent to the compressed air energy storage power station execution object. The economic dispatch instruction includes at least one of the following: compressor unit start / stop instruction, air charging regulating valve opening instruction, air release regulating valve opening instruction, expander output instruction, generator grid connection power instruction, and grid interface power allocation instruction. At the same time, the operating status data of the compressed air energy storage power station is collected, and the economic dispatch scheme is rolled over and corrected according to the operating status feedback results.

[0031] like Figure 2 As shown, this embodiment provides an economic dispatch method for a compressed air energy storage power station that incorporates uncertain information. The method uses a compressed air energy storage power station as the dispatching object. The execution objects of the compressed air energy storage power station include a compressor unit 1-1, a heat storage / exchange device 1-2, a gas storage device 1-3, an expander 1-4, a generator 1-5, and a grid / load interface 1-6. The compressor unit 1-1 consumes electrical energy and compresses air during the energy storage phase; the heat storage / exchange device 1-2 recovers, transfers, or regulates the heat generated during compression; the gas storage device 1-3 stores compressed air; the expander 1-4 uses the compressed air to expand and perform work during the energy release phase; the generator 1-5 converts the mechanical energy output from the expander 1-4 into electrical energy; and the grid / load interface 1-6 enables grid connection and output of electrical energy or supplies power to the load.

[0032] In this embodiment, the economic dispatch method does not perform static optimization based solely on a single forecast value. Instead, it incorporates renewable energy output forecasts, load forecasts, time-of-use pricing, equipment operating status, and historical operating data as inputs. Through data preprocessing, uncertainty analysis, economic dispatch model construction, optimization solution, dispatch command output, and operating status feedback, a closed-loop economic dispatch process is formed. This enables the compressed air energy storage power station to obtain a relatively stable and executable economic dispatch scheme even under conditions of renewable energy output fluctuations, load demand changes, electricity price changes, and equipment status changes.

[0033] like Figure 3As shown, at the implementation level, the specific components of a compressed air energy storage power station include a compressor unit 2-1, a charging regulating valve 2-2, a heat storage / exchange device 2-3, an air storage device 2-4, a release regulating valve 2-5, an expander 2-6, a generator 2-7, and a grid / load interface 2-8. During energy storage operation, the compressor unit 2-1 compresses air upon startup. The compressed air enters the heat storage / exchange device 2-3 via the charging regulating valve 2-2, and after heat recovery or heat exchange regulation, it enters the air storage device 2-4. During energy release operation, the air storage device 2-4 releases compressed air. The compressed air enters the expander 2-6 via the release regulating valve 2-5. The expander 2-6 drives the generator 2-7 to generate electricity, which is then output to the grid or load via the grid / load interface 2-8. This connection allows economic dispatch results to act on the energy storage side, the energy release side, and the grid-connected output side respectively, avoiding the dispatch results remaining only at the power planning level and being difficult to translate into equipment control actions.

[0034] In this embodiment, multiple operational status monitoring points are set up in the compressed air energy storage power station to collect real-time status data during the scheduling and execution process. The monitoring points include inflation flow monitoring point 2-9, inflation temperature monitoring point 2-10, energy release temperature monitoring point 2-11, pressure monitoring point 2-12, gas release flow monitoring point 2-13, equipment status monitoring point 2-14, and power monitoring point 2-15. Specifically, the following monitoring points are used: Gas flow monitoring point 2-9 collects the gas flow rate before and after entering the gas storage device 2-4 during the energy storage stage; Gas temperature monitoring point 2-10 collects the temperature of the compressed air during the energy storage stage; Energy release temperature monitoring point 2-11 collects the gas temperature before and after entering the expander 2-6 during the energy release stage; Pressure monitoring point 2-12 collects the pressure of the gas storage device 2-4; Gas release flow monitoring point 2-13 collects the gas release flow rate during the energy release stage; Equipment status monitoring point 2-14 collects the start-up / stop, operating, or fault status of the compressor unit 2-1, gas flow regulating valve 2-2, gas release regulating valve 2-5, expander 2-6, and generator 2-7; Power monitoring point 2-15 collects power data from the generator 2-7 or the grid / load interface 2-8. The data collected from these monitoring points is integrated into the operation status monitoring and feedback unit for subsequent deviation judgment, parameter correction, and rolling optimization.

[0035] In specific implementation, such as Figure 4 and Figure 5 As shown, the economic scheduling method of the present invention includes the following steps.

[0036] S1. Acquire multi-source input data for the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information, and historical operating data. New energy output forecast information may include wind power output forecast information, photovoltaic power output forecast information, or other renewable energy output forecast information; load forecast information may include grid-side load demand, user-side load demand, or park load demand; time-of-use electricity price information may include peak-hour electricity price, flat-hour electricity price, valley-hour electricity price, and real-time electricity price; equipment operating status information may include the operating status of compressor unit 2-1, the pressure status of gas storage device 2-4, the operating status of expander 2-6, the output status of generator 2-7, and the grid / load interface 2-8 grid connection status; historical operating data may include historical new energy output, historical load, historical electricity price, historical gas filling flow rate, historical gas release flow rate, historical gas storage pressure, and historical power output data.

[0037] S2 involves preprocessing the multi-source input data. Since data from different sources may differ in sampling period, data format, and units, data cleaning, outlier removal, missing data imputation, normalization, and time scale unification are necessary. Data cleaning removes obviously erroneous or duplicate data; outlier removal eliminates data outside the reasonable operating range of the equipment; missing data imputation can be achieved through interpolation between adjacent time points, imputation based on historical similar days, or imputation using a predictive model; normalization ensures that data with different units can participate in the same model calculation; and time scale unification aligns renewable energy output, load demand, time-of-use pricing, equipment status, and historical operating data to the same scheduling period. After these processes, the scheduling input data is obtained.

[0038] S3, Identify Uncertainty Information. In this embodiment, uncertainty information includes uncertainty in new energy output, load demand, time-of-use pricing, equipment operating status, and internal power plant operating status. Uncertainty in new energy output mainly stems from factors such as wind speed, solar intensity, and weather forecast errors; uncertainty in load demand mainly stems from user electricity consumption behavior, changes in production plans, or weather changes; uncertainty in time-of-use pricing mainly stems from market price fluctuations or changes in grid dispatch strategies; uncertainty in equipment operating status mainly stems from equipment start-up / shutdown status, changes in operating efficiency, or fault risks; uncertainty in the internal power plant operating status is mainly reflected in fluctuations in state quantities such as gas storage pressure, gas filling flow rate, gas release flow rate, gas filling temperature, energy release temperature, and power output.

[0039] S4 represents and fuses uncertain information. For example... Figure 6As shown, the predicted data and historical operating data are first input into the uncertainty information analysis unit. The historical predicted values ​​and actual operating values ​​are compared to construct a prediction error sequence. Then, the sources of uncertainty are identified based on the prediction error sequence, and it is determined whether the sample data meets the requirements of probabilistic modeling. When the number of historical samples is sufficient and the error distribution exhibits statistical regularity, a probability distribution method is used to represent the uncertainty information; when the number of samples is insufficient or the error distribution pattern is not obvious, an interval boundary method is used to represent the uncertainty information. Subsequently, an initial uncertainty scenario set is constructed based on the probability distribution representation results or the interval boundary representation results, and the scenario probability or confidence weight of each scenario is calculated. To avoid excessive computational load due to an excessive number of scenarios, the initial uncertainty scenario set is screened and reduced, retaining a typical scheduling scenario set that can represent the main fluctuation characteristics, and this set is used as input for constructing the economic scheduling model.

[0040] S5, construct an economic scheduling optimization model. For example... Figure 7 As shown, typical dispatch scenario sets, time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters, and risk control parameters are input into the economic dispatch model construction unit. The economic dispatch optimization model is used to determine the energy storage power, energy release power, gas storage pressure target, equipment operating status, grid-connected power plan, and reserve capacity plan of the compressed air energy storage power station in different dispatch periods. During model construction, the energy storage process, energy release process, and grid-connected output process of the compressed air energy storage power station should be considered simultaneously, so that the model output corresponds to the compressor unit 2-1, charging regulating valve 2-2, releasing regulating valve 2-5, expander 2-6, generator 2-7, and grid / load interface 2-8.

[0041] S6. Establish the objective function and constraints. The objective function aims to optimize the economic operation of the compressed air energy storage power station. It can comprehensively consider one or more of the following: electricity purchase cost, compression power consumption cost, power generation revenue, wind and solar curtailment penalty cost, equipment start-up and shutdown loss cost, power deviation penalty cost, and uncertainty risk penalty cost. The constraints include power constraints and start-up and shutdown constraints for compressor unit 2-1, pressure constraints and capacity constraints for gas storage device 2-4, opening constraints for charging regulating valve 2-2, opening constraints for releasing regulating valve 2-5, output constraints for expander 2-6, power constraints for generator 2-7, grid connection power constraints for grid / load interface 2-8, charging and discharging state switching constraints, ramping constraints, and safe operation constraints. Through these constraints, it is ensured that the optimization result is not only economical but also conforms to the actual operating boundary of the compressed air energy storage power station.

[0042] S7. Optimize and solve the economic dispatch optimization model to obtain the economic dispatch scheme. The solution method can be at least one of multi-scenario optimization, robust optimization, chance-constrained optimization, mixed integer optimization, or rolling optimization. The economic dispatch scheme includes equipment operation plan, power dispatch plan, state target plan, and reserve capacity plan. The equipment operation plan is used to determine the operating status of compressor unit 2-1, charging regulating valve 2-2, releasing regulating valve 2-5, expander 2-6, and generator 2-7 in different dispatch periods; the power dispatch plan is used to determine energy storage power, releasing power, and grid-connected power; the state target plan is used to determine the gas storage pressure, gas temperature, and flow rate targets; the reserve capacity plan is used to address dispatch deviations caused by changes in new energy output, load demand, or equipment operating status.

[0043] S8 verifies the safety and feasibility of the economic dispatch scheme. The verification includes checking whether the pressure of the gas storage device 2-4 is within the allowable range, whether the power of the compressor unit 2-1 exceeds the rated limit, whether the opening of the charging regulating valve 2-2 and the releasing regulating valve 2-5 meets the adjustment range, whether the output of the expander 2-6 meets the operating boundary, whether the power of the generator 2-7 meets the grid connection requirements, and whether the power of the grid / load interface 2-8 meets the grid dispatch or load power supply requirements. When the economic dispatch scheme meets the safety and feasibility requirements, an economic dispatch command is output; when the economic dispatch scheme does not meet the requirements, the model parameters, risk parameters, or constraints are corrected, and the solution is re-solved.

[0044] S9 outputs and executes economic dispatch instructions. These instructions include at least one of the following: compressor unit start / stop instruction (2-1), charging valve opening adjustment instruction (2-2), release valve opening adjustment instruction (2-5), expander output adjustment instruction (2-6), generator grid-connected power instruction (2-7), and grid / load interface power allocation instruction (2-8). After the dispatch instructions are output, the compressed air energy storage power station performs energy storage, energy release, or grid-connected operation according to the dispatch plan. By converting the economic dispatch results into specific equipment control instructions, the engineering feasibility of the dispatch plan can be improved.

[0045] S10 performs operational feedback and rolling corrections. For example... Figure 8 and Figure 9As shown, this embodiment employs a multi-timescale rolling scheduling mechanism combining day-ahead, intraday, and real-time scheduling. During the day-ahead scheduling phase, a day-ahead baseline scheduling plan is formed based on day-ahead forecast data and the day-ahead uncertainty scenario set. During the intraday correction phase, the day-ahead baseline scheduling plan is corrected based on intraday updated data and forecast deviations to form an intraday corrected scheduling plan. During the real-time rolling phase, the intraday corrected scheduling plan is continuously optimized based on real-time operating data and execution deviations to form real-time scheduling correction instructions. During real-time execution, the operation status monitoring and feedback unit receives data collected from charging flow monitoring point 2-9, charging temperature monitoring point 2-10, energy release temperature monitoring point 2-11, pressure monitoring point 2-12, gas release flow monitoring point 2-13, equipment status monitoring point 2-14, and power monitoring point 2-15, and calculates the deviation between the actual operating status and the economic scheduling scheme. If the deviation does not exceed a set threshold, the current scheduling execution is maintained; if the deviation exceeds the set threshold, correction parameters are generated, uncertainty information, model parameters, or constraints are updated, the solution is re-optimized, and a correction scheduling instruction is output and reissued to the power plant execution object.

[0046] In one specific embodiment, the economic dispatch method can operate according to a preset dispatch cycle. For example, a day-ahead dispatch cycle can be used, several hours can be used as the intraday correction cycle, and minutes or shorter time intervals can be used as the real-time rolling correction cycle. The day-ahead dispatch phase mainly generates a basic dispatch plan based on renewable energy output forecast information, load forecast information, time-of-use electricity price information, and historical operating data; the intraday correction phase corrects the day-ahead dispatch plan based on the updated renewable energy output, load demand, and electricity price changes; the real-time rolling phase verifies the current dispatch execution status based on real-time data collected from the gas flow monitoring point 2-9, gas temperature monitoring point 2-10, energy release temperature monitoring point 2-11, pressure monitoring point 2-12, gas release flow monitoring point 2-13, equipment status monitoring point 2-14, and power monitoring point 2-15.

[0047] When the system is in energy storage mode, the economic dispatch model prioritizes checking the operating power of compressor unit 2-1, the opening degree of gas charging regulating valve 2-2, the thermal state of thermal storage / heat exchange device 2-3, and the pressure limit of gas storage device 2-4 to avoid excessive gas charging due to surplus renewable energy or low electricity prices, which could lead to gas storage pressure exceeding the limit. When the system is in energy release mode, the economic dispatch model prioritizes checking the available pressure of gas storage device 2-4, the opening degree of gas release regulating valve 2-5, the output range of expander 2-6, the grid-connected power of generator 2-7, and the power demand of grid / load interface 2-8 to avoid insufficient gas release flow or excessive power command, which could result in power generation output failing to meet the dispatch plan.

[0048] Furthermore, when the operation status monitoring and feedback unit determines that there is a deviation between the actual power, gas storage pressure, gas charging flow rate, gas releasing flow rate, or equipment status and the scheduling plan, it first classifies the magnitude of the deviation. If the deviation is within the allowable range, the current scheduling command continues to be executed; if the deviation exceeds the preset threshold but does not meet the safety interlocking conditions, the uncertainty parameters and model constraints are updated, and the scheduling command is recalculated and corrected; if the deviation reaches the safety risk range, the operation commands of compressor unit 2-1, gas releasing regulating valve 2-5, or expander 2-6 are restricted, and priority is given to ensuring the safety of the pressure of gas storage device 2-4, the power of generator 2-7, and the output of grid / load interface 2-8. Through the above processing, the present invention can not only achieve economic optimization, but also take into account equipment safety and scheduling feasibility in actual operation.

[0049] Through the above-described embodiments, this invention can unify the fusion of uncertain information, equipment operation constraints, economic dispatch optimization, dispatch command execution, and operational status feedback. On the one hand, this invention can generate economic dispatch schemes that are more adapted to the actual operating environment based on renewable energy output, load demand, time-of-use pricing, and equipment status changes. On the other hand, this invention can use real-time operational data collected from monitoring points 2-9 to 2-15 to perform feedback correction and rolling optimization of the dispatch scheme, thereby improving the operational economy, safety, and dispatch reliability of compressed air energy storage power stations under renewable energy fluctuation environments.

[0050] The embodiments of the present invention also have the following technical effects: First, this invention integrates new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information, and historical operating data into the economic dispatch process of compressed air energy storage power stations, realizing the fusion processing of multi-source uncertain information and avoiding the problem of insufficient adaptability of dispatch schemes caused by traditional dispatch methods relying only on a single forecast value.

[0051] Second, this invention systematically represents uncertain information through methods such as prediction error statistics, probability distribution representation, interval boundary representation, scenario generation and reduction, and confidence weight fusion, enabling the scheduling model to adapt to various uncertain factors such as new energy output, load demand, electricity price changes, and equipment status fluctuations.

[0052] Third, this invention establishes an economic dispatch optimization model that conforms to the operating mechanism of compressed air energy storage power stations. It incorporates execution components such as compressor unit 2-1, charging regulating valve 2-2, heat storage / heat exchange device 2-3, air storage device 2-4, release regulating valve 2-5, expander 2-6, generator 2-7, and grid / load interface 2-8 into the dispatch constraints, so that the optimization results can be transformed into actual executable equipment control commands.

[0053] Fourth, the present invention collects operating status data through inflation flow monitoring point 2-9, inflation temperature monitoring point 2-10, energy release temperature monitoring point 2-11, pressure monitoring point 2-12, gas release flow monitoring point 2-13, equipment status monitoring point 2-14 and power monitoring point 2-15, and performs model correction and rolling optimization based on the feedback results, thereby improving the closed-loop control capability of the scheduling process.

[0054] Fifth, the present invention adopts a multi-timescale rolling scheduling mechanism that combines day-ahead, intraday, and real-time scheduling, enabling the scheduling scheme to be gradually corrected based on prediction updates and operational feedback, thus avoiding the problem that one-time static optimization cannot respond to operational disturbances in a timely manner.

[0055] Sixth, the present invention can output dispatching instructions that directly act on the execution components of the compressed air energy storage power station, which improves the engineering feasibility of economic dispatching results and enhances the operational economy, safety and dispatching reliability of the compressed air energy storage power station in the context of new energy fluctuations.

[0056] To achieve the above embodiments, such as Figure 10 As shown, this embodiment also provides an economic dispatch device 10 for compressed air energy storage power stations that integrates uncertainty information, including: The multi-source data preprocessing module 100 is used to acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. The module preprocesses the multi-source input data to obtain scheduling input data that meets a unified time scale. Uncertainty scenario construction module 200 is used to identify uncertainty information affecting the economic dispatch of compressed air energy storage power station based on the dispatch input data. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. The module performs error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a set of typical dispatch scenarios. The economic dispatch optimization solution module 300 is used to input the typical dispatch scenario set into the economic dispatch model construction unit, and combine time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters and risk control parameters to establish an economic dispatch optimization model for compressed air energy storage power plants. The economic dispatch optimization model includes an objective function with the goal of optimizing operating economy and constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints and grid interface power constraints. The module then optimizes and solves the economic dispatch optimization model to obtain an economic dispatch scheme. The scheme verification and rolling execution module 400 is used to verify the safety and executability of the economic dispatch scheme. If the requirements are met, it outputs economic dispatch instructions and sends them to the compressed air energy storage power station execution objects. The economic dispatch instructions include at least one of the following: compressor unit start / stop instructions, air charging regulating valve opening instructions, air release regulating valve opening instructions, expander output instructions, generator grid connection power instructions, and grid interface power allocation instructions. At the same time, it collects the operating status data of the compressed air energy storage power station and makes rolling corrections to the economic dispatch scheme based on the operating status feedback results.

[0057] The present invention provides an economic dispatch method and apparatus for compressed air energy storage power stations that integrates uncertain information. This method can effectively integrate multi-source uncertain information and improve the operational economy and dispatch reliability of compressed air energy storage power stations in the context of new energy fluctuations.

[0058] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 11 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0059] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0060] 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 the present invention. 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.

[0061] 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 invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An economic dispatch method for compressed air energy storage power stations that integrates uncertain information, characterized in that, include: S10, acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. Perform data preprocessing on the multi-source input data to obtain scheduling input data that meets a unified time scale. S20, based on the scheduling input data, identify the uncertainty information affecting the economic scheduling of the compressed air energy storage power station. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. Then, perform error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a typical scheduling scenario set. S30, the typical scheduling scenario set is input into the economic scheduling model construction unit. Combining time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters, and risk control parameters, an economic scheduling optimization model for compressed air energy storage power plants is established. The economic scheduling optimization model includes an objective function with optimal operating economy as the goal, as well as constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints, and grid interface power constraints. The economic scheduling optimization model is then optimized and solved to obtain an economic scheduling scheme. S40, the safety and executability of the economic dispatch scheme are checked. If the requirements are met, an economic dispatch instruction is output and sent to the compressed air energy storage power station execution object. The economic dispatch instruction includes at least one of the following: compressor unit start / stop instruction, air charging regulating valve opening instruction, air release regulating valve opening instruction, expander output instruction, generator grid connection power instruction, and grid interface power allocation instruction. At the same time, the operating status data of the compressed air energy storage power station is collected, and the economic dispatch scheme is rolled over and corrected according to the operating status feedback results.

2. The method as described in claim 1, characterized in that, S10 includes: Perform data cleaning on the multi-source input data to remove erroneous or duplicate data; Outliers were removed from data that exceeded the reasonable operating range of the equipment. Missing data were filled in by interpolation between adjacent times, filling in data on similar historical days, or filling in data using predictive models. The filled data was then normalized to eliminate differences in different units. Furthermore, the normalized data is standardized in terms of time scale, aligning new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information, and historical operating data to the same scheduling cycle, thereby obtaining scheduling input data that meets the unified time scale.

3. The method as described in claim 1, characterized in that, S20 includes: Historical forecasts and actual operating values ​​are input into the uncertainty information analysis unit for comparison to construct a forecast error sequence; Based on the prediction error sequence, identify the sources of uncertainty and determine whether the sample data meets the requirements of probability modeling; When the number of historical samples is sufficient and the error distribution has a statistical regularity, the uncertainty information is characterized by a probability distribution. When the number of samples is insufficient or the error distribution pattern is not obvious, the uncertainty information is characterized by the interval boundary method. An initial set of uncertain scenarios is constructed based on the probability distribution characterization results or interval boundary characterization results, and the scenario probability or confidence weight of each uncertain scenario is calculated. The initial set of uncertain scenarios is filtered and reduced to retain a set of typical scheduling scenarios that can represent the main fluctuation characteristics.

4. The method as described in claim 1, characterized in that, S40 includes: Check whether the pressure of the gas storage device is within the allowable range, whether the power of the compressor unit exceeds the rated limit, whether the opening of the gas charging regulating valve and the gas releasing regulating valve meets the regulation range, whether the output of the expander meets the operating boundary, whether the power of the generator meets the grid connection requirements, and whether the power of the grid interface meets the grid dispatch or load power supply requirements. When the economic scheduling scheme meets the requirements of safety and executability, an economic scheduling instruction is output; When the economic scheduling scheme does not meet the requirements of safety and feasibility, the model parameters, risk parameters or constraints are modified, and the optimization solution is re-run until the economic scheduling scheme that meets the requirements is obtained.

5. The method as described in claim 4, characterized in that, The method further includes: A multi-timescale rolling scheduling mechanism combining day-ahead, intraday, and real-time scheduling is adopted. In the day-ahead scheduling phase, a day-ahead baseline scheduling plan is formed based on day-ahead forecast data and day-ahead uncertainty scenario set. In the intraday correction phase, the day-ahead baseline scheduling plan is corrected based on intraday updated data and forecast deviation to form an intraday corrected scheduling plan. During the real-time rolling phase, the intraday revised scheduling plan is rolled out based on real-time operating data and execution deviations to form real-time scheduling correction instructions, which are then sent to the compressed air energy storage power station execution objects.

6. The method as described in claim 5, characterized in that, The method further includes: The operational status data is collected by multiple monitoring points installed in the compressed air energy storage power station; The monitoring points include a gas flow monitoring point for collecting the gas flow rate during the energy storage stage, a gas temperature monitoring point for collecting the gas temperature during the energy storage stage, a gas temperature monitoring point for collecting the gas temperature during the energy release stage, a pressure monitoring point for collecting the pressure status of the gas storage device, a gas flow monitoring point for collecting the gas flow rate during the energy release stage, an equipment status monitoring point for collecting the start-stop status, operating status, or fault status of the compressor unit, gas regulating valve, gas releasing regulating valve, expander, and generator, and a power monitoring point for collecting power data at the generator or grid interface. The operational status data collected by the multiple monitoring points are incorporated into the operational status monitoring and feedback unit. The operational status monitoring and feedback unit calculates the deviation between the actual operational status and the economic scheduling scheme. When the deviation does not exceed the set threshold, the current scheduling scheme is maintained and continues to be executed. When the deviation exceeds the set threshold, correction parameters are generated and uncertainty information, model parameters or constraints are updated. The solution is then re-optimized and the correction scheduling instruction is output.

7. The method as described in claim 5, characterized in that, The method further includes: The compressed air energy storage power station includes compressor units, heat storage / exchange devices, gas storage devices, expanders, generators, and grid interfaces; Among them, the compressor unit is used to consume electrical energy and compress air during the energy storage stage, the heat storage or heat exchange device is used to recover, transfer or regulate the heat generated during the compression process, and the air storage device is used to store compressed air. An expander is used to expand compressed air to do work during the energy release phase. A generator is used to convert the mechanical energy output by the expander into electrical energy. A grid interface is used to realize the grid connection of electrical energy or to supply power to the load.

8. An economic dispatch device for a compressed air energy storage power station that integrates uncertain information, characterized in that, include: The multi-source data preprocessing module is used to acquire multi-source input data of the compressed air energy storage power station. The multi-source input data includes new energy output forecast information, load forecast information, time-of-use electricity price information, equipment operating status information and historical operating data. The module preprocesses the multi-source input data to obtain scheduling input data that meets a unified time scale. An uncertainty scenario construction module is used to identify uncertainty information affecting the economic dispatch of compressed air energy storage power stations based on the dispatch input data. The uncertainty information includes uncertainty of new energy output, uncertainty of load demand, uncertainty of time-of-use electricity price, uncertainty of equipment operating status, and uncertainty of the internal operating status of the power station. The module performs error statistics, probability distribution characterization, or interval boundary characterization on the uncertainty information to generate a set of typical dispatch scenarios. The economic dispatch optimization solution module is used to input the typical dispatch scenario set into the economic dispatch model construction unit, and combine time-of-use electricity price parameters, load demand parameters, power plant operating status parameters, equipment performance parameters and risk control parameters to establish an economic dispatch optimization model for compressed air energy storage power plants. The economic dispatch optimization model includes an objective function with the goal of optimizing operating economy and constraint conditions including compressor unit power constraints, gas storage device pressure constraints, gas charging regulating valve regulation constraints, gas releasing regulating valve regulation constraints, expander output constraints, generator power constraints and grid interface power constraints. The module optimizes and solves the economic dispatch optimization model to obtain an economic dispatch scheme. The scheme verification and rolling execution module is used to verify the safety and executability of the economic dispatch scheme. If the requirements are met, it outputs economic dispatch instructions and sends them to the compressed air energy storage power station execution objects. The economic dispatch instructions include at least one of the following: compressor unit start / stop instructions, air charging regulating valve opening instructions, air releasing regulating valve opening instructions, expander output instructions, generator grid connection power instructions, and grid interface power allocation instructions. At the same time, it collects the operating status data of the compressed air energy storage power station and makes rolling corrections to the economic dispatch scheme based on the operating status feedback results.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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