Time-phased variable-target peak regulation optimization scheduling method for auxiliary power system of pumped storage unit

Abstract

The invention provides a time-phased variable-target peak regulation optimization scheduling method for an auxiliary power system of a pumped storage unit, and aims to excavate the flexibility potential of a traditional power system unit, improve the new energy consumption capability of the power system and reduce the number of times of switching the pumping state of the pumped storage unit so as to optimize the operation economy of the system. The method comprises the following steps: quantitatively calculating the flexibility vacancy of the power system; the operation modes of the thermal power generating unit are determined, and the operation modes comprise an economic operation mode, a peak regulation operation mode and a coordinated operation mode arranged between the economic operation mode and the peak regulation operation mode and used for achieving smooth transition; and loading a thermal power-pumped storage coordinated time-phased adaptive scheduling model, substituting the flexibility vacancy of the power system, a thermal power generating unit operation mode switching instruction and a corresponding pumped storage unit control strategy mode into the scheduling model to carry out optimization solution, and finally outputting a scheduling optimization result. Wherein on the basis of the determined operation mode of the thermal power generating unit, peak regulation is assisted by controlling drainage power generation or pumped storage operation of the pumped storage unit.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling technology, specifically a time-based adaptive variable target pumped storage unit auxiliary power system peak shaving real-time optimization scheduling method. Background Technology

[0002] Against the backdrop of large-scale centralized grid connection of new energy sources, the urgent need for grid flexibility in large energy bases is becoming increasingly prominent. Limited by the harsh natural environment and high infrastructure construction costs in the desert regions, there is a severe shortage of fast-response energy storage resources. Traditional power system dispatching models suffer from structural contradictions such as underutilized peak-shaving potential and high costs associated with energy storage-assisted peak-shaving. Under these circumstances, tapping into the flexibility and control potential of existing conventional generating units and optimizing the configuration and operating costs of energy storage devices have become key measures to address the current power system flexibility requirements.

[0003] Existing research on energy storage-assisted peak shaving in power systems mainly focuses on the capacity configuration and economic evaluation of energy storage devices, with limited research on exploring the peak shaving potential of power systems by changing the dispatching and operation modes. Furthermore, traditional dispatching methods for energy storage-assisted peak shaving require energy storage devices to smooth out the volatility brought by new energy sources in real time, placing high demands on the capacity of the energy storage devices and their ability to quickly adjust their charge and discharge states. Therefore, a dispatching and operation mode is needed to leverage the advantages of pumped storage units—large capacity and low operating costs—to assist the power system in peak shaving and absorbing new energy sources. Summary of the Invention

[0004] To enhance the power system's capacity to absorb new energy sources, this invention proposes a time-varying target-based peak-shaving optimization scheduling method for pumped storage units. This method aims to tap the flexibility potential of traditional power system units, reduce the number of times pumped storage units switch between pumping and generating states, thereby optimizing the system's operational economy.

[0005] Firstly, a time-segmented variable-target pumped-storage unit-assisted power system peak-shaving optimization scheduling method is proposed, including: quantitatively calculating the power system flexibility deficit; determining the operating modes of thermal power units, including an economic operating mode, a peak-shaving operating mode, and a coordinated operating mode set between the economic and peak-shaving operating modes to achieve a smooth transition; loading a time-segmented adaptive scheduling model of thermal power-pumped storage coordination, and incorporating the power system flexibility deficit, thermal power unit operating mode switching commands, and corresponding pumped-storage unit control strategy modes into the scheduling model for optimization. The solution is obtained, and the final output is the scheduling optimization result. Based on the determined operating mode of the thermal power units, peak shaving is assisted by controlling the pumped storage units to release water for power generation or to perform pumped storage operations. The scheduling model includes three objective functions for the thermal power-pumped storage system under economic operation mode, peak shaving operation mode, and coordinated operation mode: the objective function in the economic operation mode measures the economic indicators of the power system, and its optimization objective is to maximize the systemic economic indicators; the objective function in the peak shaving operation mode combines the objectives of renewable energy consumption and economic efficiency, with the wind power consumption objective having a significant weight. The weight is no less than that of the economic optimization objective. Its optimization objective is to prioritize maximizing wind power consumption while ensuring a certain level of economic efficiency, in order to address the uncertainty of new energy output. The objective function in the coordinated operation mode stage combines two objectives: economic efficiency and downward flexibility adjustment capability, with the economic efficiency objective having a higher weight. The weight of the downward flexibility adjustment target is less than And the weight of economic objectives Weighting of downward flexibility adjustment target It is adaptive and its optimization goal is to prioritize improving the downward flexibility adjustment capability of thermal power units while ensuring economic efficiency, so as to cope with the sharp changes in the output of new energy.

[0006] In a second aspect, a computer is proposed, comprising: a processor; and a memory including one or more computer program modules; wherein the one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the pumped storage unit auxiliary power system peak-shaving optimization scheduling method for achieving the time-varying target.

[0007] Thirdly, a peak-shaving optimization scheduling method for pumped storage auxiliary power systems is proposed, which stores non-transitory computer-readable instructions and, when executed by a computer, can achieve the time-varying target. Attached Figure Description

[0008] Figure 1This is a flowchart of a time-sharing adaptive variable target pumped storage unit auxiliary power system peak-shaving optimization scheduling method according to an embodiment of the present invention.

[0009] Figure 2a This is the output curve of a thermal power unit in an economic operation mode according to an embodiment of the present invention.

[0010] Figure 2b This is the output curve of a thermal power unit under the independent peak-shaving mode in one embodiment of the present invention.

[0011] Figure 2c This is the output curve of a thermal power unit during auxiliary peak shaving of a pumped storage unit in one embodiment of the present invention.

[0012] Figure 3 This is a pumped storage unit output curve and reservoir capacity ratio change curve in one embodiment of the present invention.

[0013] Figure 4 This is a diagram of the number of thermal power units put into deep peak shaving operation and the power curve according to an embodiment of the present invention. Detailed Implementation

[0014] Figure 1 This paper presents a time-varying, variable-objective, real-time optimization scheduling method for pumped-storage units (PSUs) to assist power system peak shaving. First, the flexibility and regulation capacity of the power system are quantitatively calculated. Then, based on the calculation results and the real-time grid absorption demand, the operating modes of conventional units during the scheduling period are divided into three types: economic, peak shaving, and coordinated operation, and corresponding scheduling optimization objectives are set. Based on the operating characteristics of PSUs, an optimized scheduling model for PSUs to assist power system peak shaving is established. This invention employs an adaptive variable-objective optimization method to optimize the peak shaving of conventional thermal power units in conjunction with PSUs under different operating modes, reducing the number of times thermal power units enter deep peak shaving states, tapping the potential of the power system to absorb new energy sources while ensuring the system's economic efficiency. The method is described in detail below.

[0015] In terms of data acquisition, real-time output data and basic parameters of conventional generator units, basic parameters of pumped storage units and supporting reservoirs, short-term and ultra-short-term forecast data of wind power, photovoltaic power generation and load, and day-ahead dispatch plan output data of thermal power units can all be collected by the power grid dispatch center.

[0016] Step 1: Using short-term and ultra-short-term forecast data of new energy sources and loads, conventional unit parameters and day-ahead planned output, assess the degree of flexibility deficit in the power system (grid) in real time.

[0017] Quantitative calculations of power system flexibility are performed on a short timescale. First, a model for the flexibility requirements of new energy sources and loads is established, as shown in equations (1) and (2): (1) In the formula: For a moment Forecasted load values; and They are time points Upper and lower limits of load fluctuation; This represents the maximum prediction error coefficient for the load. and They are time points The load’s upward and downward flexibility requirements.

[0018] (2) In the formula: For a moment Forecast values ​​for new energy sources; and They are time points The upper and lower limits of fluctuations in new energy sources; This represents the maximum prediction error coefficient for new energy sources. and They are time points The need for upward and downward flexibility in new energy sources.

[0019] Then, a power system flexibility supply model is established, the mathematical expression of which is shown in equation (3): (3) In the formula: and They are time points The power grid's upward and downward flexibility requirements. For a moment No. The output of the thermal power unit; and They are time points No. The upper and lower limits of conventional technologies for thermal power units in Taiwan; and They are time points The upward and downward flexibility of thermal power units; and They are time points No. The rate of ascent and descent of the thermal power unit.

[0020] Finally, the power system flexibility deficit is quantitatively calculated and expressed as Equation (4): (4) In the formula: and They are time points The grid's upside and downside flexibility margins; when At that time, the power system experienced a downward flexibility deficit.

[0021] Step 2: Based on the current level of flexibility deficit in the power system (grid), classify the operating modes of conventional thermal power units to determine whether they have entered a deep peak-shaving state. No flexibility deficit, economic mode; a flexibility deficit exists, peak-shaving mode is entered.

[0022] From the perspective of thermal power units, the operating mode of thermal power units is determined by comparing the real-time demand output with the day-ahead planned output adjustment range: when the real-time demand output is within the day-ahead planned output adjustment range, the corresponding period adopts the economic operation mode; when the real-time demand output exceeds the real-time output adjustment range or is lower than the normal minimum technical output, the corresponding period enters the peak-shaving operation mode. The criteria for switching the operating mode of thermal power units are shown in equation (5): (5) In the formula: The operating mode (economic / peak shaving) is indicated by 0 / 1. for The thermal power units are scheduled to operate at the current time. for The power output of thermal power units is constantly in demand. for Always allow for deviations from the planned output of thermal power units. It provides the minimum technical output for conventional thermal power units.

[0023] To address the potential issue of sufficient peak-shaving capacity but insufficient ramp-up speed, making it difficult to respond promptly to renewable energy consumption demands during the transition between operating modes of thermal power units, a coordinated operation mode is defined as a buffer phase between the economic operation mode and the peak-shaving operation mode. Under this mode, the thermal power units maintain their conventional technical output range (consistent with the economic operation mode), but their overall flexibility and adaptability to load fluctuations are enhanced through optimized operating strategies.

[0024] When the difference between the equivalent load demand under the ultra-short-term forecast output of wind power and the conventional minimum technical output of thermal power units is less than the total downward ramp capacity of the power system per unit time period, the thermal power units should enter the coordinated operation mode. The triggering criterion for this mode is Equation (6), and its core idea is: if the power system can meet the demand for new energy consumption by adjusting the overall flexibility adjustment capability of thermal power units, it does not need to directly enter the deep peak shaving state, but can achieve a smooth transition through the coordinated operation mode.

[0025] (6) In the formula, For the first The unit's ability to climb downhill per unit time.

[0026] Step 3: Based on the conventional operating status of thermal power units, by controlling the water release and power generation and pumped storage operations of pumped storage units, the power system is assisted in achieving peak shaving and new energy consumption targets, further reducing the number of times thermal power units enter deep peak shaving state.

[0027] The optimized operation mode for real-time dispatching of pumped storage units to auxiliary thermal power units is as follows: Pumped storage unit control strategies are divided into two categories: prediction error control and peak-shaving state control. By dynamically adjusting the operating states of pumped storage (pumping mode) and power generation (power generation mode), flexible management of the reservoir capacity of the pumped storage power station can be achieved, thereby supporting the system's peak-shaving needs and improving the capacity for renewable energy consumption.

[0028] (1) Prediction error control When the difference between the predicted output of ultra-short-term thermal power and the planned output before the day exceeds the upward adjustment range reserved for thermal power units, pumped storage units switch to water release and power generation operation to supplement the power demand of the grid. At this time, the water release and power generation of the pumped storage units is calculated according to formula (7): (7) In the formula, The discharge power of the pumped storage power station during time period t is... , They will contribute to the day-ahead and ultra-short-term plans of thermal power units, respectively. This represents the allowable fluctuation amount.

[0029] (2) Peak shaving state control When the predicted output of ultra-short-term thermal power is lower than the sum of the minimum technical output of conventional thermal power units, it means that in the scenario where pumped storage units do not assist in peak shaving, the power system will have one or more thermal power units needing to enter the deep peak shaving state of oil injection. At this time, pumped storage units assist thermal power units in peak shaving through pumped storage (charging) operation, reducing the number of thermal power units entering deep peak shaving, thereby avoiding economic losses caused by oil injection operation. At this time, the pumped storage unit is in pumped storage operation state, and its charging power is calculated according to formula (8).

[0030] (8) In the formula, The pumping power consumption of the pumped storage power station during time period t is the power consumption for pumping water. This represents the maximum power consumption for pumping water in a pumped storage unit.

[0031] Step 4: Use the time-sharing adaptive variable target pumped storage unit auxiliary peak-shaving scheduling model (also known as: thermal power-pumped storage coordinated time-sharing adaptive scheduling model) to perform optimization solution.

[0032] Based on the power system operation requirements and considering the various operating modes of thermal power units during different operating periods, a time-sharing adaptive scheduling model for thermal power-pumped storage is constructed, and its mathematical expression is shown in equation (9): (9) In the formula, Revenue from the sale of electricity by thermal power units; The cost of coal for thermal power units; The environmental remediation costs resulting from coal-fired emissions; For the operating costs of pumped storage units; , The weights for wind power consumption and economic optimization are respectively, and the weight of the wind power consumption sub-objective should not be less than that of the economic optimization sub-objective, i.e. ,and ; for The amount of wind power absorbed during the time period; Combustion cost for thermal power units operating under deep peak shaving conditions, unit: yuan; The environmental remediation costs resulting from combustion-supporting processes; For unit loss costs; for t Maximum wind power generation during the specified time period; for t The maximum revenue of a thermal power unit under the optimal economic plan with the goal of economic optimization within a given time period; For the first Taiwanese unit The ability to adjust flexibly during the downward time period; for t The total revenue of a thermal power unit under the optimal economic scheme with economic optimization as the objective during the specified time period. for t Maximum downward flexibility adjustment margin for thermal power units during specific time periods; , These are the weighting coefficients for the economic optimization objective and the downward flexibility adjustment objective, respectively. Since the main objective of this model is to cope with sharp changes in new energy output, the weight of the economic optimization objective should be less than the weight of the downward flexibility adjustment objective. .

[0033] It can be seen that the scheduling model contains three objective functions. The objective function of the time (economic operation mode stage) measures the economic indicators of the system. Its optimization objective is to maximize the system's economic indicators (i.e., maximize the net profit of thermal power unit electricity sales revenue and various costs).

[0034] The objective function for the peak-shaving operation phase combines two sub-objectives: renewable energy consumption and economic efficiency. These two indicators are adjusted using weighting coefficients, with the weighting coefficient for the wind power consumption sub-objective being no less than that for the economic optimization sub-objective. The optimization objective is to prioritize maximizing wind power consumption while ensuring a certain level of economic efficiency, in order to address the uncertainty of renewable energy output.

[0035] The objective function for this phase (coordinated operation mode) combines two sub-objectives: economic efficiency and flexibility adjustment capability. Since the primary objective of this mode is to cope with sudden changes in renewable energy output, the weight coefficient for the economic objective should be less than the weight coefficient for downward flexibility adjustment. This weight coefficient is adaptively variable. The optimization objective is to prioritize improving the downward flexibility adjustment capability of thermal power units while ensuring economic efficiency, in order to cope with sudden changes in renewable energy output.

[0036] The operating cost and regulation capacity function of thermal power units are defined by equations (10) and (11): (10) (11) In the formula, This refers to the power generation of a single thermal power unit. To optimize the time period length, 15 minutes can be used; This represents the number of thermal power units. The number of wind turbine units; , , For the first Parameters of the coal cost curve for a Taiwanese thermal power unit; , The desulfurization and denitrification operating costs per unit of power supply for units operating within the conventional peak-shaving output range; Price per unit of oil; This represents the number of generating units in deep peak-shaving mode. , For the first Parameters of fuel consumption curve for Taiwanese generator unit; The environmental treatment cost per unit of fuel oil under deep peak shaving conditions for the generating unit; This represents the actual operating loss coefficient of a thermal power plant. For the first Purchase cost of a thermal power unit; For the first Taiwan thermal power units The rotation time from the cracking cycle, this value is related to the output of the thermal power unit. Related; To meet the needs of power grid flexibility Taiwanese unit Efforts are made at all times.

[0037] The operating cost function of a pumped storage unit is defined by equation (12): (12) In the formula, The total operating cost of the pumped storage unit during time period t. The start-up and shutdown cost of the pumped storage unit during time period t. The peak-shaving operating cost of the pumped storage unit during time period t; and These represent the startup costs of the pumped storage unit when it is in power generation mode and pumping mode, respectively. Let g be a 0-1 variable representing the power generation status of the pumped storage power station and its generating unit g in time period t, where 1 indicates that it is in power generation status, and 0 otherwise. For pumped storage power stations and units, there are 0-1 variables representing the pumping status during time period t. A value of 1 indicates that the power station is in the pumping state, and a value of 0 otherwise. and These represent the pumping electricity price and the electricity sales price for pumped storage units participating in peak shaving, respectively. and These represent the power generation and pumping power of the pumped storage unit g at time t, respectively.

[0038] In coordinated operation mode, an adaptive variable weighting method is used to change the weights of the objective function. The weights are adaptively determined based on the overall operating status of the thermal power units in the current power system. The baseline value is In the downward flexibility output range Inside, The value of is determined by the total output status of the thermal power units within the interval, as shown in equation (11): (13) A deep peak-shaving model for thermal power units and constraints for pumped storage units were constructed. The model was linearized using piecewise linearization and the Big-M method. The linearized model was then solved using the CPLEX solver via the YALMIP toolbox on the Matlab platform.

[0039] Power balance constraints: (14) Thermal power unit constraints: 1) Output constraint (15) (16) In the formula, For the unit Minimum technical output under normal conditions. For the unit Minimum technical output under deep peak shaving conditions For the unit The maximum technical output.

[0040] 2) Climbing constraints (17) 3) Output deviation constraints under economic operation mode (18) In the formula, For thermal power units At any moment Real-time output, For thermal power units At any moment The recent work plan, For the unit The allowable fluctuation ratio.

[0041] 4) Tie line power constraints (19) In the formula, For a moment Real-time power transmission of the communication line , These represent the minimum and maximum transmission power of the tie line, respectively.

[0042] Constraints of pumped storage units: 1) Power generation and pumping constraints (20) In the formula: and These are the minimum and maximum power generation capacities of the pumped storage unit g, respectively. and These are the minimum and maximum pumping power of the pumped storage unit g, respectively.

[0043] 2) Start-stop constraints (twenty one) 3) Reservoir water quantity constraints (twenty two) In the formula: The water storage in the reservoir during time period t. and These are its upper and lower limits, respectively; and These are the ratios of average water volume converted to electricity for pumping and power generation, respectively.

[0044] 4) Total power generation constraints (twenty three) The present invention also provides an embodiment of a computer. The computer includes a processor and a memory. The memory is used to store non-transitory computer-readable instructions (e.g., one or more computer program modules). The processor is used to execute the non-transitory computer-readable instructions, which, when executed by the processor, can perform one or more steps in the time-varying target pumped-storage unit auxiliary power system peak-shaving optimization scheduling method described above. The memory and processor can be interconnected via a bus system and / or other forms of connection mechanisms.

[0045] For example, a processor can be a central processing unit (CPU), a graphics processing unit (GPU), or other form of processing unit with data processing and / or program execution capabilities. For instance, a CPU can be based on x86 or ARM architectures. A processor can be a general-purpose processor or a special-purpose processor, and it can control other components in a computer to perform desired functions.

[0046] For example, memory can include any combination of one or more computer program products, which can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory can include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact optical disc read-only memory (CD-ROM), USB storage, flash memory, etc. One or more computer program modules can be stored on the computer-readable storage medium, and the processor can run one or more computer program modules to implement various functions of the computer.

[0047] This invention also provides a computer-readable storage medium for storing non-transitory computer-readable instructions. When executed by a computer, these instructions can implement one or more steps in the above-described time-varying target pumped-storage unit auxiliary power system peak-shaving optimization scheduling method. When the time-varying target pumped-storage unit auxiliary power system peak-shaving optimization scheduling method provided in this embodiment is implemented in software and sold or used as an independent product, it can be stored in a computer-readable storage medium. For further details regarding the storage medium, please refer to the corresponding description of memory in computer systems above; it will not be repeated here.

[0048] For example, the computer configuration required to run the software corresponding to the pumped storage unit auxiliary power system peak shaving optimization scheduling method of the present invention with time-varying targets is as follows: the processor can be any Intel or AMD x86-64 processor, supporting four logical cores and AVX2 instruction set; minimum 30GB hard disk; minimum 4GB RAM; operating system Windows 10 Professional 64-bit; optimization software: Yalmip + Cplex 12.10; scientific computing software: MATLAB 2018a.

[0049] Implementation Case: This embodiment uses a regional wind power-thermal power-pumped storage combined regional power grid system to simulate and verify the effectiveness of the proposed strategy. The system includes nine thermal power units with installed capacities of three 330MW units, four 300MW units, and two 220MW units. The pumped storage units have a pumping capacity of 50MW and a generating capacity of 50MW.

[0050] Scenario 1: Economic dispatch of conventional generating units for peak shaving.

[0051] Scenario 2: Adaptive variable target peak shaving scheduling of conventional units across multiple time periods.

[0052] Scenario 3: Pumped storage units assist conventional units in peak shaving, which is the peak shaving mode proposed in this invention.

[0053] Results Analysis: In the stand-alone peak-shaving mode of thermal power units, the output of thermal power units in multi-period adaptive variable target optimization scheduling is shown in Figure 2(b). Compared with the output of thermal power units in the conventional peak-shaving scheduling scenario shown in Figure 2(a), the output of units during the conventional peak-shaving period and the pure economic optimization show a high degree of consistency when the coordinated scheduling mode is introduced. Thermal power units operate less frequently at their highest technical output, which means more power generation space is left for other thermal power plants, and more downward adjustment margin is also provided to better cope with sudden changes in wind power. Furthermore, considering the economic cost losses incurred by thermal power units due to reserving sufficient absorption capacity, the reserved absorption margin is minimized as much as possible while meeting absorption demand, maximizing the economic optimization of the units.

[0054] In the peak-shaving mode of the pumped storage unit assisting the thermal power unit, the output curve of the thermal power unit is shown in Figure 2(c), and the output plan of the pumped storage power station unit is as follows: Figure 3 As shown in Figure 2(b), during periods 1-4 and 66-75, multiple units operated in deep peak shaving mode to varying degrees. However, compared to Figure 2(b), the number of thermal power units operating in deep peak shaving mode was significantly reduced, thereby reducing the amount of oil injected into the units for deep peak shaving and thus reducing the peak shaving loss cost of the thermal power units. As shown in Figure (3), when wind power generation is high and load demand is relatively low, the pumped storage power station operates at its maximum pumped storage power to ensure maximum absorption of wind power while minimizing the number and depth of thermal power units entering deep peak shaving mode.

[0055] The number of thermal power units entering the oil injection depth peak shaving and the peak shaving depth values ​​at each time period before and after the addition of pumped storage units are as follows: Figure 4 As shown, during different periods of deep peak shaving for thermal power units, except for period 69 when pumped storage units are used to reduce wind curtailment, the number of thermal power units entering deep peak shaving state in other periods is reduced by 1 to 2 units to varying degrees. The peak shaving demand when the units enter deep peak shaving is also reduced accordingly, which improves the overall operating economy of the system and thermal power units.

Claims

1. A time-segmented variable-target peak-shaving optimization scheduling method for pumped-storage auxiliary power systems, characterized in that, include: Quantitative calculation of power system flexibility deficit; The operating modes of thermal power units are determined, including economic operating mode, peak-shaving operating mode, and a coordinated operating mode set between economic operating mode and peak-shaving operating mode to achieve a smooth transition. A time-sharing adaptive scheduling model for thermal power-pumped storage is loaded. The power system flexibility deficit, thermal power unit operating mode switching commands, and corresponding pumped storage unit control strategies are substituted into the scheduling model for optimization. The final output is the optimized scheduling result. Based on the determined thermal power unit operating modes, peak shaving is assisted by controlling the pumped storage units to release water for power generation or perform pumped storage operations. The scheduling model includes three objective functions for the thermal power-pumped storage system under economic operation mode, peak shaving operation mode, and coordinated operation mode: The objective function of the economic operation mode measures the economic indicators of the power system, and its optimization objective is to maximize the economic indicators of the power system. The objective function for the peak-shaving operation mode combines two objectives: renewable energy consumption and economic efficiency, with the wind power consumption objective having a specific weight. The weight is no less than that of the economic optimization objective. Its optimization objective is to prioritize maximizing wind power consumption while ensuring a certain level of economic efficiency, in order to cope with the uncertainty of new energy output. The objective function for the coordinated operation mode phase combines two objectives: economy and downward flexibility adjustment capability, with the economy objective having a specific weight. The weight of the downward flexibility adjustment target is less than And the weight of economic objectives Weighting of downward flexibility adjustment target It is adaptive and its optimization goal is to prioritize improving the downward flexibility adjustment capability of thermal power units while ensuring economic efficiency, so as to cope with the sharp changes in the output of new energy.

2. The method according to claim 1, characterized in that, The mathematical formula for the scheduling model is: In the formula, This indicates that the thermal power unit is in the economic operation mode stage; This indicates that the thermal power unit is in the peak-shaving operation mode. This indicates that the thermal power unit is in the coordinated operation mode phase; Revenue from the sale of electricity by thermal power units; The cost of coal for thermal power units; The environmental remediation costs resulting from coal-fired emissions; For the operating costs of pumped storage units; , These are the weighting coefficients for wind power consumption and economic optimization, respectively. ,and ; for The amount of wind power absorbed during the time period; Combustion cost for thermal power units operating under deep peak shaving conditions, unit: yuan; The environmental remediation costs resulting from combustion-supporting processes; For unit loss costs; for t Maximum wind power generation during the specified time period; for t The maximum revenue of a thermal power unit under the optimal economic plan with the goal of economic optimization within a given time period; For the first Taiwanese unit The ability to adjust flexibly during the downward time period; for t The total revenue of a thermal power unit under the optimal economic scheme with economic optimization as the objective during the specified time period. for t Maximum downward flexibility adjustment margin for thermal power units during specific time periods; , These are the weighting coefficients for the economic optimization objective and the downward flexibility adjustment objective, respectively. .

3. The method according to claim 1, characterized in that, The operating mode of thermal power units is determined by comparing the real-time demand output with the daily planned output adjustment range.

4. The method according to claim 3, characterized in that, When the real-time demand output of thermal power units is within the range of the day-ahead power generation plan output adjustment, the corresponding period adopts the economic operation mode.

5. The method according to claim 3, characterized in that, When the real-time demand output of a thermal power unit exceeds the real-time output adjustment range of the thermal power unit, or is lower than the normal minimum technical output of the thermal power unit, the corresponding period will enter the peak-shaving operation mode.

6. The method according to claim 1, characterized in that, When the difference between the equivalent load demand under the ultra-short-term forecast output of wind power and the conventional minimum technical output of thermal power units is less than the total downward ramp capacity of the power system per unit time period, the thermal power units enter the coordinated operation mode.

7. The method according to claim 1, characterized in that, When the difference between the predicted output of ultra-short-term thermal power and the planned output of the day exceeds the reserved upward adjustment range, the pumped storage unit switches to the prediction error control mode and maintains the state of releasing water to generate electricity.

8. The method according to claim 1, characterized in that, When the predicted output of ultra-short-term thermal power is lower than the minimum technical output of the thermal power unit, the pumped storage unit switches to the peak-shaving control mode to maintain the pumped storage state.

9. A computer, characterized in that, Include: processor; Memory, including one or more computer program modules; The one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the pumped storage unit auxiliary power system peak shaving optimization scheduling method with time-varying targets as described in any one of claims 1-8.

10. A method for storing non-transitory computer-readable instructions, characterized in that, When the non-transitory computer-readable instructions are executed by a computer, they can realize the peak-shaving optimization scheduling method for pumped storage auxiliary power systems with time-varying targets as described in any one of claims 1-8.

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