A design method and system of a compressed air energy storage power station operation strategy

Abstract

The application discloses a design method and system of a compressed air energy storage power station operation strategy, comprising the following steps: S1, collecting power grid load statistical data of an area where the power station is located; S2, generating an expected load curve in the entire operation cycle according to the collected power grid load statistical data, by adjusting the distribution of holidays and weekdays and daily load extreme values; S3, based on the expected load curve, using a dynamic programming method to iteratively calculate the charging and discharging strategy of each time node, and then calculating the optimal long-term operation strategy of the power station, so as to evaluate the long-term stability and tightness of the underground storage of the compressed air energy storage power station. Based on the dynamic programming method, the periodic change of the power load and its influence are fully considered, the long-term optimal operation strategy of the power station is calculated, and the long-term stability and tightness of the underground storage are better evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of compressed air energy storage power stations, and particularly relates to a design method and system for a compressed air energy storage power station operation strategy. BACKGROUND

[0002] At present, underground compressed air energy storage technology has been widely applied in the world, especially in power peak shaving and stable power grid operation, and has shown significant advantages. During the power peak, air is released to drive the turbine, and the generated electric energy is put into the market. Through the transformation and storage of electric energy, the contradiction between clean energy supply and market demand is alleviated. Therefore, the utilization efficiency of clean energy is improved, and the utilization proportion of high-carbon emission power generation methods such as coal-fired and gas-fired is reduced, thereby reducing environmental pollution.

[0003] The operation strategy of the power station, including the timing and quantity of charging and discharging, directly determines the real-time peak shaving effect of the power station. At the same time, the operation strategy also changes the internal pressure and thermal environment of the storage, which affects the long-term energy storage capacity of the power station by affecting the thermodynamic field and the surrounding rock mechanical field in the underground storage. Therefore, determining the long-term operation strategy of the power station is the basis for evaluating the economy of the compressed air energy storage power station and the long-term stability and airtightness of the underground storage. At present, there are mainly two kinds of methods to determine the operation strategy of the compressed air energy storage power station:

[0004] (1) Simple fixed strategy method: This is a relatively traditional operation strategy design method, which is based on the conventional understanding that the daytime power demand is large and the nighttime demand is small. The operation strategy is simply designed as charging and storing electricity at night and discharging electricity during the day, so as to carry out simulation and evaluation of the long-term operation reliability of the underground storage. The advantage of this method is simple to implement. However, the actual power demand is closely related to the local industrial structure, climate and holiday distribution, so the simplified operation strategy cannot well realize the "peak shaving and valley filling" of the local power market, and may even exacerbate the local power load fluctuation. Therefore, the above-mentioned simplified strategy cannot well represent the actual operation state of the power station (its charging and discharging is adjusted according to the real-time power load), and thus the long-term stability and airtightness of the underground storage calculated will lack authenticity and accuracy.

[0005] (2) Real-time optimization method: This method considers the uncertainty of the power market price and the storage capacity of the power station, and adjusts the operation strategy of the power station in real time to achieve the best economic benefit. This method is closer to the actual production conditions and can flexibly respond to power load fluctuations. However, when simulating and calculating the long-term stability and airtightness of the underground storage, the difficulty of this method is that the life cycle of the power station is usually more than 30 years, and the charging and discharging strategy in the whole cycle must be known conditions. However, the real-time adjustment based on this method conflicts with the requirement of long-term prediction, and it is difficult to accurately simulate the energy storage reliability change in the whole life cycle of the power station. SUMMARY

[0006] To solve the above problems, the application provides a design method and system for the operation strategy of a compressed air energy storage power station. Based on the dynamic programming method, the periodic changes in power load and their effects are fully considered to calculate the long-term optimal operation strategy of the power station, thereby ensuring better evaluation of the long-term stability and tightness of the underground storage.

[0007] The first aspect of the application provides a design method for the operation strategy of a compressed air energy storage power station, comprising the following steps:

[0008] S1: collecting power grid load statistical data in the area where the power station is located;

[0009] S2: generating an expected load curve in the entire operation period according to the collected power grid load data, by adjusting the distribution of holidays and weekdays and the daily load extreme value;

[0010] S3: based on the expected load curve, using the dynamic programming method to iteratively calculate the charging and discharging strategy of each time node, and then calculating the optimal long-term operation strategy of the power station, so as to evaluate the long-term stability and tightness of the underground storage of the compressed air energy storage power station.

[0011] For example, in the design method for the operation strategy of a compressed air energy storage power station provided in an embodiment, in S1, collecting power grid load data in the area where the power station is located includes: typical daily load changes, typical load differences between holidays and weekdays, and daily load extreme values in the statistical period.

[0012] For example, in the design method for the operation strategy of a compressed air energy storage power station provided in an embodiment, in S2, in the expected load curve, the entire research period T is converted into N discrete time points, and the interval of the time points is The power grid load at these time points is used as the discrete input condition of the dynamic programming method in S3.

[0013] For example, in the design method for the operation strategy of a compressed air energy storage power station provided in an embodiment, in S3, the variance of the power load of the power grid to which the compressed air energy storage power station belongs is used as the objective function f of the programming, and the operation strategy of the power station is to minimize this characteristic value after being connected to the power grid, and satisfies the following formula:

[0014]

[0015] P g (t) * =P g (t)+P c (t)-P d (t);

[0016] wherein P g (t) and P are the grid load before and after the action of the power station, respectively; P c (t) and P d (t) are the power output and received by the grid to and from the power station in the time interval [t, t+Δt]; N is the number of sampling points.

[0017] For example, in the design method of the operation strategy of the compressed air energy storage power station provided in an embodiment, the constraint conditions of the power station include: battery capacity constraint, different time charging and discharging constraint, power variation constraint, and operation state variation constraint.

[0018] For example, in the design method of the operation strategy of the compressed air energy storage power station provided in an embodiment, the battery capacity constraint is that the electric energy storage of the underground storage in the specified working interval has upper and lower limits, and satisfies the following formula:

[0019] A min ≤ A(t) ≤ A max ;

[0020] wherein A(t) is the electric energy stored in the underground storage at time t; A min and A max are the upper and lower limits of the electric energy stored in the underground storage, respectively, and A(t) varies with the charging and discharging of the power station, and satisfies the following formula:

[0021]

[0022] wherein η c and η d are the efficiencies of the power output and received by the grid to and from the power station, respectively.

[0023] For example, in the design method of the operation strategy of the compressed air energy storage power station provided in an embodiment, the different time charging and discharging constraint is a nonlinear constraint, the nonlinear constraint is linearized by using Big-M method, and satisfies the following formula:

[0024]

[0025] U c (t) + U d (t) ≤ 1;

[0026] wherein U and U are the maximum values of the power output and received by the grid to and from the power station, respectively; U c (t) and U d(t) is a binary variable, which is 1 when the power station charges / discharges in the time interval [t, t+Δt], otherwise 0; M is a large enough number, taking 10 7 .

[0027] For example, in the design method of the compressed air energy storage power station operation strategy provided in an embodiment, the power variation constraint satisfies the following formula:

[0028]

[0029]

[0030] wherein, and are the limit variation rates of the charging / discharging power of the power station.

[0031] For example, in the design method of the compressed air energy storage power station operation strategy provided in an embodiment, the operation state variation constraint is that the transition between the charging, stopping and discharging states of the power station within the operation period is limited within a certain number of times, and satisfies the following formula:

[0032]

[0033] wherein, U tran is a quantity used to record the number of times of the operation state variation of the power station within the entire period, the transition from charging / discharging to stopping and its reverse process, and the value is 1, the transition between charging and discharging and its reverse process, and the value is 2, is the upper limit of the number of state transitions.

[0034] The second aspect of the present application provides a design system of a compressed air energy storage power station operation strategy, comprising a power grid load data acquisition module, a load curve analysis module and an operation strategy optimization module, the power grid load data acquisition module is used to collect the power grid load statistical data of the region where the power station is located; the load curve analysis module generates the expected load curve within the entire operation period by adjusting the distribution of holidays and weekdays and the daily load extreme value according to the collected power grid load data; the operation strategy optimization module takes the analyzed load curve as the input data of the dynamic programming method, performs strategy optimization, iteratively calculates the charging and discharging strategy of each time node by using the dynamic programming method with the target of minimizing the power grid load fluctuation, and further calculates the optimal long-term operation strategy of the power station, so as to be used for evaluating the long-term stability and airtightness of the underground storage of the compressed air energy storage power station.

[0035] The method and system for designing an operation strategy of a compressed air energy storage power station provided by some embodiments of the present application have the beneficial effects that: the present application can realize more accurate long-term operation strategy optimization of an underground storage; the core of the present application is to minimize the fluctuation of power grid load, to optimize a more reasonable and accurate long-term operation mode, and to provide more real input conditions for later evaluation of the long-term stability and airtightness of the underground storage. Compared with a simplified or real-time adjusted operation strategy, the present application is more suitable for simulation of the operation reliability of an underground storage of a full life cycle energy storage power station. Overall, the present application provides a feasible operation strategy calculation method for more accurate evaluation of the long-term stability and airtightness of an underground storage of a compressed air energy storage power station, solves the deficiencies of the prior art in simulation and evaluation of the long-term operation reliability of a storage, and helps to significantly improve the operation efficiency of an underground storage compressed air energy storage power station and the long-term safety of the underground storage. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0037] Figure 1 The flowchart of the method for designing an operation strategy of a compressed air energy storage power station of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0039] Unless otherwise defined, technical terms or scientific terms used in the present disclosure shall have the ordinary meaning as understood by a person having ordinary skill in the art to which the present disclosure pertains. The terms "first", "second", and similar terms used in the present disclosure do not denote any order, quantity, or importance, but are used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are used only to represent relative positional relationships, and when the absolute positions of the described objects are changed, the relative positional relationships can also be changed accordingly.

[0040] The first aspect of the present application provides a design method of an air compression energy storage power station operation strategy, aiming to solve the following key problems existing in the current design of the underground storage air compression energy storage power station operation strategy:

[0041] 1. Simplified strategy difficult to adapt to complex power market peak shaving demand: The existing fixed strategy method (such as night charging and day discharging) does not fully consider the influence of power demand differences in different regions, industrial structure, and climate conditions, and cannot effectively realize the function of "peak shaving and valley filling" of the power grid. The present application proposes a new method that can more reasonably cope with these complex factors to adapt to the peak shaving demand of the local power market of the power station.

[0042] 2. Lack of certainty of long-term operation strategy: Real-time optimization method can adjust the operation strategy of the power station in real time to obtain economic benefits, but due to the need to rely on real-time power load and market fluctuations, it is difficult to provide stable and known operation strategies. Therefore, when simulating and evaluating the whole life cycle operation of the power station, this method is difficult to apply to simulate the stability and airtightness of the underground storage of the energy storage power station under long-term operation. The method proposed in the present application can provide a long-term stable operation strategy to overcome this deficiency.

[0043] The design method of the air compression energy storage power station operation strategy of the present application is shown in Figure 1 and includes the following steps:

[0044] S1 Collect the power grid load data of the region where the power station is located;

[0045] The data collected includes the power grid load data of the area where the power station is located, such as typical daily load changes, typical load differences between holidays and working days, and daily load extremes within the statistical period. The data source is the power data published by authoritative institutions. From the published power data, you can find the typical daily load changes (divided into holidays and working days) of the local (province where the power station is located) power grid and the daily load extremes within the statistical period.

[0046] Based on the collected power grid load data, S2 adjusts the distribution of holidays and working days and the daily load extremes within the statistical period, assuming that the load change pattern of the local power grid remains unchanged in the long term, and generates the expected load curve for the entire operating cycle as the input condition for subsequent strategy optimization.

[0047] Specifically, the expected load curve for the local area within a year can be obtained by adjusting the daily load curve amplitude based on the distribution of holidays and working days within a year and the daily load extreme value. Considering the local climate, industrial structure and holiday distribution, it can be assumed that the power grid maintains this typical load change pattern throughout the entire operating cycle of the power plant.

[0048] To ensure computational efficiency, this application transforms the entire research period T into N discrete time points, with time intervals of... The grid load at these moments serves as the discrete input condition for the dynamic programming method.

[0049] This application constructs a projected load curve based on typical grid load variation patterns as input for dynamic programming, ensuring that the strategy matches local electricity demand and adapts to load changes and strategy optimization throughout the power station's entire lifecycle (over 30 years). Compared to complex methods relying on real-time adjustments, this application is more suitable for long-term evaluation and simulation. By utilizing the regularity of the load curve, it simplifies the handling of complex real-time electricity market fluctuations, making it more applicable to the long-term operation planning of energy storage power stations.

[0050] Based on the expected load curve, S3 uses dynamic programming to minimize grid load fluctuations and iteratively calculates the charging and discharging strategies at each time point to maximize the effect on stabilizing the local power load curve. This allows for the calculation of the optimal long-term operation strategy for the power station, which is then used to evaluate the long-term stability and airtightness of the underground storage tank of the compressed air storage power station.

[0051] This application uses dynamic programming to calculate the optimal timing and scale of power plant charging and discharging, aiming to minimize grid load fluctuations. Unlike traditional simple charging and discharging strategies, this application maximizes the power plant's peak-shaving capacity. By combining the power plant's operation strategy with the goal of minimizing grid load fluctuations, it improves the economic efficiency of power plant operation and grid stability, and helps to more realistically predict the long-term safety of underground storage facilities.

[0052] The load prediction model can improve the calculation accuracy. If the calculation accuracy is to be improved, a more complex load prediction model can be used to replace the expected load curve generation method to more accurately reflect the long-term fluctuations of the power market. At the same time, the load prediction model can be continuously corrected combined with the actual operation data to further optimize the operation strategy.

[0053] The expected load curve is taken as an example to illustrate the input conditions of the dynamic programming method as follows:

[0054] The variance reflects the degree of deviation of a random variable from its mean, thereby representing its fluctuation. In the present application, the variance of the power load of the power grid to which the compressed air energy storage power station belongs is taken as the objective function f of the programming, and the operation strategy of the power station is to minimize this characteristic value after being connected to the power grid and to satisfy the following formula:

[0055]

[0056] P g (t) * =P g (t)+P c (t)-P d (t);

[0057] wherein P g (t) and P are the power grid loads before and after the action of the power station, respectively; P c (t) and P d (t) are the power outputted by the power grid to the power station and the power received by the power station from the power grid in the time interval [t, t+Δt], respectively; and N is the number of sampling points.

[0058] As for the constraint conditions of the power station, the following items are included:

[0059] (1) Battery capacity constraint

[0060] The underground storage has upper and lower limits of the stored electric energy in the specified working interval and satisfies the following formula:

[0061] A min ≤A(t)≤A max ;

[0062] wherein A(t) is the stored electric energy in the underground storage at time t; A min and A max are the upper and lower limits of the stored electric energy in the underground storage, respectively; and A(t) changes with the charging and discharging of the power station and satisfies the following formula:

[0063]

[0064] wherein η c and η dThese represent the efficiency of power output from the power grid to the power plant and the efficiency of power received from the power plant, respectively.

[0065] (2) Simultaneous charge and discharge constraints

[0066] There are losses in the energy conversion process of power plants. At the same time, charging and discharging not only fail to improve the load, but also cause energy waste.

[0067] The simultaneous charge-discharge constraint is a nonlinear constraint. The Bi gM method is used to linearize the nonlinear constraint, satisfying the following equation:

[0068]

[0069] U c (t)+U d (t)≤1;

[0070] in, and These represent the maximum power output from the power grid to the power plant and the maximum power received from the power plant, respectively; U c (t) and U d (t) is a binary variable, where 1 represents the power station charging / discharging within the time interval [t, t+Δt], and 0 otherwise; M is a sufficiently large number, taken as 10. 7 .

[0071] (3) Power variation constraints

[0072] Due to equipment limitations, there is a lag during the start-up / shutdown of the power plant, requiring a certain amount of time to reach the preset power. The power change constraint satisfies the following formula:

[0073]

[0074] in, and These represent the limiting rates of change of the charging / discharging power of the power station.

[0075] (4) Constraints on changes in operating status

[0076] During the operating cycle, the transitions between the three states of charging, stopping, and discharging of the power station are limited to a certain number of times to avoid reducing the service life of the compressor and turbine due to excessively frequent start-stop operations.

[0077] The constraints on changes in operating state satisfy the following equation:

[0078]

[0079] Among them, U tranis used to record the number of changes in the operating state of the power station during the entire cycle, from charging / discharging to stopping and vice versa, this value is 1, from charging to discharging and vice versa, is counted as 2, because it is necessary to pass through the "stop" state, is the upper limit of the number of state transitions.

[0080] The above battery capacity constraint, different charging and discharging constraint, power change constraint and operating state change constraint are all mathematical expressions of the dynamic programming method of the present application, and the optimal operation strategy of the compressed air energy storage power station in the whole life cycle, including the timing and power of charging and disarging, can be solved by using the dynamic programming method. After the charging and discharging rates and the pressure change in the underground storage are converted, they can be used as input conditions to calculate the mechanical stability and airtightness of the underground storage of the compressed air energy storage power station during the entire operation cycle. The calculation results are closer to the actual state of the storage than the results based on the simplified strategy, which is beneficial to better analyze and predict the energy storage capacity and reliability of the storage.

[0081] The design method of the operation strategy of the compressed air energy storage power station of the present application overcomes the limitations of the existing evaluation methods: the existing methods either rely on simplified power demand assumptions, leading to evaluation results of the storage deviating from the actual situation, or use random adjustment strategies, which are difficult to use for long-term operation simulation of the storage. The present application more accurately determines the stable operation strategy in a longer cycle based on the expected load curve of the power grid, thereby overcoming these technical limitations, laying a foundation for truly predicting and simulating the mechanical stability and airtightness of the underground storage of the energy storage power station in the whole life cycle, and ensuring the accuracy and operability of the evaluation results of the storage.

[0082] The second aspect of the present application provides a design system for the operation strategy of a compressed air energy storage power station, comprising a power grid load data acquisition module, a load curve analysis module and an operation strategy optimization module. The power grid load data acquisition module is used to collect power grid load statistical data of the area where the power station is located. The load curve analysis module generates an expected load curve during the entire operation cycle by adjusting the distribution of holidays and weekdays and the daily load extreme value based on the collected power grid load statistical data. The operation strategy optimization module uses the analyzed load curve as input data for dynamic programming, optimizes the strategy, and iteratively calculates the charging and discharging strategy of each time node by using dynamic programming to minimize the power grid load fluctuation, and then calculates the optimal long-term operation strategy of the power station, which is used to evaluate the long-term stability and airtightness of the underground storage of the compressed air energy storage power station.

[0083] The method and system proposed in the present application can more accurately determine the long-term operation strategy of the power station, and lay a foundation for truly predicting and simulating the mechanical stability and airtightness of the underground storage of the energy storage power station in the whole life cycle.

[0084] While embodiments of the application have been disclosed in connection with the above specification and drawings this description is not intended to limit the scope of the application and many modifications, enhancements, alternatives, and variations will become apparent to those skilled in the art from this disclosure. Accordingly, it is intended that the application not be limited to the described embodiments, but that it include all variations falling within the scope of the claims, and their equivalents.

Claims

1. A method for designing an operating strategy of a compressed-air energy storage power plant, characterized in that, The method comprises the following steps: S1 collecting power grid load statistical data of the area where the power station is located; S2 generating an expected load curve in the entire operation cycle according to the collected power grid load statistical data, by adjusting the distribution of holidays and weekdays and the daily load extreme value; S3 based on the expected load curve, using dynamic programming method to minimize the power grid load fluctuation as the target, iteratively calculating the charging and discharging strategy of each time node, and then calculating the optimal long-term operation strategy of the power station, for evaluating the long-term stability and tightness of the underground storage of the compressed air energy storage power station; The constraint conditions of the power station include: battery capacity constraint, different time charging and discharging constraint, power change constraint and operation state change constraint; The battery capacity constraint is that the underground storage has upper and lower limits of electric energy storage in the specified working range, and satisfies the following formula: ； wherein, Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; ； wherein with respectively the efficiency of the grid to output and receive power to and from the power plant; The different time charging and discharging constraint is a nonlinear constraint, which is linearized by using Big-M method, and satisfies the following formula: ； ； ； wherein, Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; 7 ; The power change constraint satisfies the following formula: ； ； wherein, with respectively the limit rate of change of the charging / discharging power of the power plant; The operation state change constraint is that the conversion between the charging, stopping and discharging states of the power station is limited within a certain number of times in the operation cycle, and satisfies the following formula: ； wherein, is a quantity used to record the number of changes in the state of the power station during the entire period, from charging / discharging to stopping and vice versa, this value is 1, from charging to discharging and vice versa, is counted as 2, is the upper limit of the number of state changes.

2. The method of claim 1, wherein the compressed air energy storage plant operation strategy is designed to: In the S1, the collection of power grid load statistical data of the area where the power station is located includes: typical daily load change, typical load difference between holidays and weekdays, and daily load extreme value in the statistical cycle.

3. The method of claim 2, wherein the compressed air energy storage plant operation strategy is designed to: In S2, in the expected load curve, the full study period T is converted into N discrete time points, the interval of the time points being the grid load at these time points as discrete input conditions for the dynamic programming method in S3.

4. The method of claim 3, wherein the compressed air energy storage plant operation strategy is designed to, In the S3, the variance of the power load of the power grid to which the compressed air energy storage power station belongs is taken as the target function f of the programming, and the operation strategy of the power station is to minimize this characteristic value after accessing the power grid, and satisfies the following formula: ； ； wherein, with are the grid loads before and after the action of the power plant, respectively; with are the power output and received by the grid to and from the power plant within the time interval of [t - T, t]. N is the number of sampling points.

5. A system for designing an operating strategy of a compressed-air energy storage power plant, characterized in that It comprises: A power grid load data acquisition module for collecting power grid load statistical data of the area where the power station is located; A load curve analysis module for generating an expected load curve in the entire operation cycle according to the collected power grid load statistical data, by adjusting the distribution of holidays and weekdays and the daily load extreme value; An operation strategy optimization module for taking the analyzed load curve as the input data of the dynamic programming method, performing strategy optimization, using the dynamic programming method to minimize the power grid load fluctuation as the target, iteratively calculating the charging and discharging strategy of each time node, and then calculating the optimal long-term operation strategy of the power station, for evaluating the long-term stability and tightness of the underground storage of the compressed air energy storage power station; The constraint conditions of the power station include: battery capacity constraint, different time charging and discharging constraint, power change constraint and operation state change constraint; The battery capacity constraint is that the underground storage has upper and lower limits of electric energy storage in the specified working range, and satisfies the following formula: ； wherein, Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; Qs(t) is the amount of electricity stored in the underground storage at time t; ； wherein with respectively the efficiency of the grid to output and receive power to and from the power plant; The different time charging and discharging constraint is a nonlinear constraint, which is linearized by using Big-M method, and satisfies the following formula: ； ； ； wherein, Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; 7 Pgridmax and Pgridmin are the maximum and minimum power that the grid can supply to the power plant and vice versa; The power change constraint satisfies the following formula: ； ； wherein, with are the limit rates of change of the charging / discharging power of the power plant, respectively; The operation state change constraint is that the conversion between the charging, stopping and discharging states of the power station is limited within a certain number of times in the operation cycle, and satisfies the following formula: ； wherein, is a quantity used to record the number of changes in the state of the power station during the entire cycle, from charging / discharging to stopping and vice versa, this value is 1, from charging to discharging and vice versa, is counted as 2, is the upper limit of the number of state changes.

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