Design method and system for operation strategy of compressed air energy storage power station

Abstract

The invention discloses a design method and system for an operation strategy of a compressed air energy storage power station. The design method comprises 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 whole operation period by adjusting distribution of holidays and festivals and workdays and daily load extreme values according to the collected power grid load statistical data; s3, on the basis of the expected load curve, iteratively calculating a charging and discharging strategy of each time node by using a dynamic planning method and taking minimization of power grid load fluctuation as a target, and further calculating an optimal long-term operation strategy of the power station so as to evaluate the long-term stability and leakproofness of the underground reservoir of the compressed air energy storage power station; the method is based on the dynamic planning method, fully considers the periodic change of the power load and the influence of the periodic change, calculates the long-term optimal operation strategy of the power station, and guarantees the better evaluation of the long-term stability and leakproofness of the underground storage.

Description

Technical Field

[0001] The present application relates to the technical field of compressed gas energy storage power stations, and in particular to a design method and system for an operation strategy of a compressed gas energy storage power station. Background Art

[0002] At present, underground compressed air energy storage technology has been widely used around the world, especially in terms of power peak regulation and stable grid operation. It releases air to drive turbines during peak power consumption, and the generated electricity is put into the market. Through the conversion and storage of electric energy, the contradiction between the supply of clean energy and market demand is alleviated. Therefore, the efficiency of clean energy utilization has been improved, and the utilization ratio of high-carbon emission power generation methods such as coal and gas has been reduced, thereby reducing environmental pollution.

[0003] The operation strategy of the power station, including the timing and amount of charging and discharging, directly determines the real-time peak-shaving effect of the power station. At the same time, the operation strategy will also change the internal pressure and thermal environment of the reservoir, and affect the long-term energy storage capacity of the power station by affecting the thermodynamic field and surrounding rock mechanical field in the underground reservoir. Therefore, determining the long-term operation strategy of the power station is the basis for evaluating the economic feasibility of the compressed gas energy storage power station and the long-term stability and airtightness of the underground reservoir. At present, there are two main mainstream methods for determining the operation strategy of the compressed gas energy storage power station:

[0004] (1) Simple fixed strategy method: This is a more traditional operation strategy design method. Based on the conventional understanding that electricity demand is high during the day and low at night, the operation strategy is simply designed to charge and store electricity at night and discharge electricity during the day, so as to simulate and evaluate the long-term operation reliability of underground storage. The advantage of this method is that it is easy to implement. However, the actual electricity demand is closely related to the local industrial structure, climate and holiday distribution. Therefore, the simplified operation strategy cannot effectively achieve "peak shaving and valley filling" in the local electricity market, but may aggravate the local power load fluctuation. Therefore, the above simplified strategy cannot well represent the actual operation status of the power station (its charging and discharging is adjusted in real time according to the power load), so the long-term stability and airtightness of the underground storage calculated will lack authenticity and accuracy.

[0005] (2) Real-time optimization method: This method takes into account the uncertainty of electricity market prices and power plant reserves, and adjusts the power plant operation strategy in real time to achieve the best economic benefits. This method is closer to actual production conditions and can flexibly respond to power load fluctuations. However, when simulating the long-term stability and airtightness of underground reservoirs, this method is difficult to meet: since the life cycle of a power plant is usually more than 30 years, the filling and discharging strategy throughout the entire cycle must be a known condition, and the premise of this method based on real-time adjustment conflicts with the requirements of long-term prediction, making it difficult to accurately simulate the changes in energy storage reliability throughout the life cycle of the power plant. Summary of the invention

[0006] In order to solve the above problems, the present application provides a design method and system for the operation strategy of a compressed gas energy storage power station. Based on the dynamic programming method, the cyclical changes of the power load and its influence are fully considered, and the long-term optimal operation strategy of the power station is calculated to ensure better evaluation of the long-term stability and airtightness of the underground reservoir. The technical solution is as follows:

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

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

[0009] S2 generates the expected load curve for the entire operation cycle based on the collected grid load data by adjusting the distribution of holidays and working days and the daily load extremes;

[0010] Based on the expected load curve, S3 uses dynamic programming to minimize grid load fluctuations, iteratively calculates the charging and discharging strategies at each time node, 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 reservoir of the compressed gas energy storage power station.

[0011] For example, in the design method of the compressed gas energy storage power station operation strategy provided in one embodiment, in S1, the grid load data of the power station area is collected including: typical daily load changes, typical load differences between holidays and weekdays, and daily load extremes within the statistical period.

[0012] For example, in the design method of the compressed gas storage power station operation strategy provided in one embodiment, in S2, in the expected load curve, the full study period T is converted into N discrete moments, and the interval of the moments is The grid loads at these moments serve as discrete input conditions for the dynamic programming method in S3.

[0013] For example, in the design method of the compressed gas storage power station operation strategy provided by one embodiment, in S3, the variance of the power load of the power grid to which the compressed gas storage power station belongs is used as the planning objective function f, and the operation strategy of the power station is to minimize this characteristic value after connecting to the power grid and satisfy the following formula:

[0014]

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

[0016] Among them, P g (t) are the grid loads before and after the power station is affected; P c (t) and P d (t) is the power output from the power grid to the power station and the power received 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 compressed gas energy storage power station operation strategy provided in one embodiment, the constraints on the power station include: battery capacity constraints, non-simultaneous charging and discharging constraints, power change constraints and operating state change constraints.

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

[0019] A min ≤A(t)≤A max ;

[0020] Where A(t) is the amount of electricity stored in the underground reservoir at time t; A min With A max are the upper and lower limits of the amount of electricity stored in the underground reservoir, respectively. A(t) changes with the charging and discharging of the power station and satisfies the following formula:

[0021]

[0022] Among them, η c With η d They are the efficiency of the power grid exporting to and receiving power from the power station, respectively.

[0023] For example, in the design method of the compressed gas energy storage power station operation strategy provided in one embodiment, the non-simultaneous charging and discharging constraint is a nonlinear constraint, and the nonlinear constraint is linearized using the BigM method to satisfy the following formula:

[0024]

[0025] U c (t)+U d (t)≤1;

[0026] in, and are the maximum values ​​of power output from the power grid to the power station and power received from the power station; U c (t) and U d(t) is a binary variable, which is 1 when it represents the power station charging / discharging in the time interval [t, t+Δt], otherwise it is 0; M is a sufficiently large number, 10 7 .

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

[0028]

[0029]

[0030] in, and They are respectively the limiting change rates of the charging / discharging power of the power station.

[0031] For example, in the design method of the compressed gas energy storage power station operation strategy provided in one embodiment, the operation state change constraint is that within the operation cycle, the conversion between the three states of charging, stopping and discharging of the power station is limited to a certain number of times and satisfies the following formula:

[0032]

[0033] Among them, U tran It is used to record the number of changes in the power station's operating status during the entire cycle. The value of the power station's conversion from charging / discharging to stopping and its reverse process is 1, and the conversion from charging to discharging and its reverse process is counted as 2. The upper limit of the number of state transitions.

[0034] The second aspect of the present application provides a design system for the operation strategy of a compressed gas-storage power station, including a power grid load data acquisition module, a load curve analysis module and an operation strategy optimization module, wherein the power grid load data acquisition module is used to collect power grid load statistical data in the area where the power station is located; the load curve analysis module generates an expected load curve within the entire operation cycle based on the collected power grid load data by adjusting the distribution of holidays and working days and the daily load extreme values; the operation strategy optimization module uses the analyzed load curve as input data of a dynamic programming method to perform strategy optimization, and uses the dynamic programming method to iteratively calculate the charging and discharging strategies of each time node with the goal of minimizing power grid load fluctuations, 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 reservoir of the compressed gas-storage power station.

[0035] The beneficial effects brought about by a design method and system for the operation strategy of a compressed air energy storage power station provided by some embodiments of the present application are as follows: the present application can achieve more accurate optimization of the long-term operation strategy of the underground reservoir: the core of the present application is to optimize a more reasonable and accurate long-term operation mode based on the minimization of grid load fluctuations, thereby providing more realistic input conditions for the later evaluation of the long-term stability and airtightness of the underground reservoir. Compared with simplified or real-time adjusted operation strategies, the present application is more suitable for simulation of the operation reliability of underground reservoirs of energy storage power stations throughout the life cycle. In general, the present application provides a practical operation strategy calculation method for more accurately evaluating the long-term stability and airtightness of underground reservoirs of compressed air energy storage power stations, solves the deficiencies of the prior art in the simulation and evaluation of the long-term operation reliability of reservoirs, and helps to significantly improve the operation efficiency of underground reservoir compressed air energy storage power stations and the long-term safety of underground reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 This is a flow chart of the design method of the compressed gas energy storage power station operation strategy of this application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

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

[0041] 1. Simplified strategies are difficult to adapt to the peak-shaving needs of complex power markets: Existing fixed strategy methods (such as charging at night and discharging during the day) do not fully consider the differences in power demand in different regions, the impact of industrial structure and climatic conditions, and cannot effectively achieve the function of "peak shaving and valley filling" of the power grid. This application proposes a new method that can more reasonably deal with these complex factors to adapt to the peak-shaving needs of the local power market of the power station.

[0042] 2. Lack of certainty in long-term operation strategies: Although the real-time optimization method can adjust the power plant operation strategy in real time to obtain economic benefits, it is difficult to provide a stable and known operation strategy because it relies on real-time power load and market fluctuations. Therefore, when simulating and evaluating the operation of the power plant throughout its life cycle, this method is difficult to apply to simulating the stability and airtightness of the underground reservoir of the energy storage power station under long-term operation. The method proposed in this application can provide a long-term stable operation strategy to overcome this shortcoming.

[0043] The design method of the compressed gas storage power station operation strategy of this application is as follows Figure 1 As shown, the following steps are included:

[0044] S1 collects grid load data in the area where the power station is located;

[0045] The grid load data collected in the area where the power station is located include: typical daily load changes, typical load differences between holidays and weekdays, and daily load extremes within the statistical period. The data source is the power data published by authoritative organizations. From the published power data, you can query the local (province where the power station is located) power grid's typical daily load changes (divided into holidays and weekdays) and daily load extremes within the statistical period.

[0046] S2 generates the expected load curve for the entire operation cycle based on the collected grid load data, by adjusting the distribution of holidays and working days and the daily load extremes within the statistical period, assuming that the load change law of the local grid remains unchanged for a long time, as the input condition for subsequent strategy optimization;

[0047] Specifically, by adjusting the amplitude of the daily load curve based on the distribution of holidays and working days in a year and the daily load extreme value, the expected load curve for the local area in a year can be obtained; taking into account 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 station.

[0048] To ensure computational efficiency, this application converts the entire research cycle T into N discrete moments, with the interval between moments being The grid loads at these moments are used as discrete input conditions for the dynamic programming method.

[0049] This application constructs an expected load curve based on the typical change law of power grid load as the input condition of dynamic programming to ensure that the strategy matches the local electricity demand and adapts to the load changes and strategy optimization during the entire life cycle of the power station (more than 30 years). Compared with complex methods that rely on real-time adjustments, this application is more suitable for long-term evaluation and simulation. The regularity of the load curve is utilized to simplify the complex real-time electricity market fluctuation processing, making it more suitable for 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, iteratively calculates the charging and discharging strategies for each time node to ensure that the effect of stabilizing the local power load curve is maximized, and then calculates the optimal long-term operation strategy of the power station to evaluate the long-term stability and airtightness of the underground reservoir of the compressed gas energy storage power station.

[0051] This application uses dynamic programming to minimize grid load fluctuations and calculate the optimal timing and scale of power station charging and discharging. Different from the traditional simple charging and discharging strategy, this application can maximize the peak-shaving capacity of the power station, and improve the economic efficiency of power station operation and grid stability by combining the power station operation strategy with the goal of minimizing grid load fluctuations, which helps to more realistically predict the long-term safety of underground reservoirs.

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

[0053] The following is an example of using the expected load curve as the input condition of the dynamic programming method:

[0054] The variance reflects the degree to which a random variable deviates from its mean, thereby characterizing its fluctuation. In this application, the variance of the power load of the power grid to which the compressed gas storage power station belongs is used as the planning objective function f. The operation strategy of the power station is to minimize this characteristic value after connecting to the power grid and satisfy the following formula:

[0055]

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

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

[0058] The constraints of the power station include the following:

[0059] (1) Battery capacity constraints

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

[0061] A min ≤A(t)≤A max ;

[0062] Where A(t) is the amount of electricity stored in the underground reservoir at time t; A min With A max are the upper and lower limits of the amount of electricity stored in the underground reservoir, respectively, and A(t) changes with the charging and discharging of the power station and satisfies the following formula:

[0063]

[0064] Among them, η c With η dThey are the efficiency of the power grid exporting to and receiving power from the power station, respectively.

[0065] (2) Constraints on simultaneous charging and discharging

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

[0067] The non-simultaneous charging and discharging constraint is a nonlinear constraint. The BigM method is used to linearize the nonlinear constraint and satisfy the following formula:

[0068]

[0069] U c (t)+U d (t)≤1;

[0070] in, and are the maximum values ​​of power output from the power grid to the power station and power received from the power station; U c (t) and U d (t) is a binary variable, which is 1 when it represents the power station charging / discharging in the time interval [t, t+Δt], otherwise it is 0; M is a sufficiently large number, 10 7 .

[0071] (3) Power variation constraints

[0072] Due to equipment limitations, there is a delay when the power station starts / stops operation, and it takes a certain amount of time to reach the preset power. The power change constraint satisfies the following formula:

[0073]

[0074] in, and They are respectively the limiting change rates of the charging / discharging power of the power station.

[0075] (4) Operation status change constraints

[0076] During the operating cycle, the conversion between the three states of charging, stopping and discharging of the power station is limited to a certain number of times to avoid reducing the service life of the compressor and turbine due to too frequent starting and stopping.

[0077] The running state change constraint satisfies the following formula:

[0078]

[0079] Among them, U tranIt is used to record the number of times the power station's operating status changes during the entire cycle. The value of the power station's conversion from charging / discharging to stopping and its reverse process is 1. The conversion from charging to discharging and its reverse process is counted as 2, because it will inevitably pass through the "stop" state in the meantime. The upper limit of the number of state transitions.

[0080] The above-mentioned battery capacity constraints, non-simultaneous charging and discharging constraints, power change constraints and operating state change constraints are all mathematical expressions of the dynamic programming method of the present application. The dynamic programming method can be used to solve the optimal operating strategy of the compressed gas storage power station during the entire life cycle, including the timing and power of charging and discharging. After converting them into charging and discharging rates and changes in the internal pressure of the underground reservoir, they can be used as input conditions. Combined with numerical methods, the mechanical stability and airtightness of the underground reservoir of the compressed gas storage power station during the entire operating cycle are calculated. The calculation results are closer to the actual state of the reservoir than the results obtained based on the simplified strategy, which is conducive to better analysis and prediction of the energy storage capacity and reliability of the reservoir.

[0081] The design method of the compressed gas energy storage power station operation strategy of this application overcomes the limitations of existing evaluation methods: existing methods either rely on simplified power demand assumptions, resulting in reservoir evaluation results that deviate from actual conditions; or adopt random adjustment strategies, which are difficult to use for long-term reservoir operation simulation. This application overcomes these technical limitations by using the expected load curve of the power grid as a basis to more accurately determine the stable operation strategy over a longer period, laying the foundation for truly predicting and simulating the mechanical stability and airtightness of the underground reservoir of the energy storage power station throughout its life cycle. Operation reliability issues such as the foundation ensure the accuracy and operability of the reservoir evaluation results.

[0082] A second aspect of the present application provides a design system for the operation strategy of a compressed gas-storage power station, comprising a power grid load data acquisition module, a load curve analysis module and an operation strategy optimization module, wherein the power grid load data acquisition module is used to collect power grid load statistical data in the area where the power station is located; the load curve analysis module generates an expected load curve within the entire operation cycle based on the collected power grid load statistical data, by adjusting the distribution of holidays and working days and the daily load extreme values; the operation strategy optimization module uses the analyzed load curve as input data of a dynamic programming method to perform strategy optimization, and uses the dynamic programming method to iteratively calculate the charging and discharging strategies of each time node with the goal of minimizing power grid load fluctuations, 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 reservoir of the compressed gas-storage power station.

[0083] The method and system proposed in this application can more accurately determine the long-term operation strategy of the power station, laying the foundation for truly predicting and simulating operational reliability issues such as the mechanical stability and airtightness of the underground reservoir of the energy storage power station throughout its life cycle.

[0084] Although the implementation scheme of the present application has been disclosed as above, it is not limited to the applications listed in the specification and implementation modes, and it can be fully applicable to various fields suitable for the present application. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present application is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for designing an operation strategy of a compressed gas energy storage power station, characterized in that: The following steps are involved: S1 collects statistical data of power grid load in the area where the power station is located; S2 generates the expected load curve for the entire operation cycle based on the collected grid load statistics by adjusting the distribution of holidays and working days and the daily load extremes; Based on the expected load curve, S3 uses dynamic programming to minimize grid load fluctuations, iteratively calculates the charging and discharging strategies at each time node, 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 reservoir of the compressed gas energy storage power station.

2. The design method of the compressed gas energy storage power station operation strategy according to claim 1 is characterized in that: In S1, the statistical data of the power grid load in the area where the power station is located are collected, including: typical daily load changes, typical load differences between holidays and weekdays, and daily load extremes within the statistical period.

3. The design method of the compressed gas energy storage power station operation strategy according to claim 2 is characterized in that: In S2, in the expected load curve, the full study period T is converted into N discrete moments, and the interval between the moments is The grid loads at these moments serve as discrete input conditions for the dynamic programming method in S3.

4. The design method of the compressed gas energy storage power station operation strategy according to claim 3 is characterized in that: In S3, the variance of the power load of the power grid to which the compressed gas energy storage power station belongs is used as the planning objective function f. The operation strategy of the power station is to minimize this characteristic value after connecting to the power grid and satisfy the following formula: P g (t) * =P g (t)+P c (t)-P d (t); Among them, P g (t) are the grid loads before and after the power station is affected; P c (t) and P d (t) is the power output from the power grid to the power station and the power received from the power station in the time interval [t, t+Δt]; N is the number of sampling points.

5. The design method of the compressed gas energy storage power station operation strategy according to claim 4 is characterized in that: The constraints on the power station include: battery capacity constraints, simultaneous charging and discharging constraints, power change constraints and operating status change constraints.

6. The design method of the compressed gas energy storage power station operation strategy according to claim 5 is characterized in that: The battery capacity constraint is that the electric energy storage capacity of the underground storage tank in the specified working range has upper and lower limits and satisfies the following formula: A min ≤A(t)≤A max ; Where A(t) is the amount of electricity stored in the underground reservoir at time t; A min With A max are the upper and lower limits of the amount of electricity stored in the underground reservoir, respectively. A(t) changes with the charging and discharging of the power station and satisfies the following formula: Among them, η c With η d They are the efficiency of the power grid exporting to and receiving power from the power station, respectively.

7. The design method of the compressed gas energy storage power station operation strategy according to claim 5 is characterized in that: The non-simultaneous charging and discharging constraint is a nonlinear constraint. The Big-M method is used to linearize the nonlinear constraint and satisfy the following formula: U c (t)+U d (t)≤1; in, and are the maximum power output from the power grid to the power station and the maximum power received from the power station; U c (t) and U d (t) is a binary variable, which is 1 when it represents the power station charging / discharging in the time interval [t, t+Δt], otherwise it is 0; M is a sufficiently large number, 10 7 .

8. The design method of the compressed gas energy storage power station operation strategy according to claim 5 is characterized in that: The power variation constraint satisfies the following equation: in, and They are respectively the limiting change rates of the charging / discharging power of the power station.

9. The design method of the compressed gas energy storage power station operation strategy according to claim 5 is characterized in that: The operating state change constraint is that within the operating cycle, the conversion between the charging, stopping and discharging states of the power station is limited to a certain number of times and satisfies the following formula: Among them, U tran It is used to record the number of changes in the power station's operating status during the entire cycle. The value of the power station's conversion from charging / discharging to stopping and its reverse process is 1, and the conversion from charging to discharging and its reverse process is counted as 2. The upper limit of the number of state transitions.

10. A design system for compressed gas energy storage power station operation strategy, characterized in that: include: The power grid load data collection module is used to collect power grid load statistics in the area where the power station is located; The load curve analysis module generates the expected load curve for the entire operation cycle based on the collected grid load statistics, by adjusting the distribution of holidays and working days and the daily load extremes; The operation strategy optimization module uses the analyzed load curve as the input data of the dynamic programming method to perform strategy optimization. The dynamic programming method is used to minimize the grid load fluctuation, and the charging and discharging strategies for each time node are iteratively calculated. Then, the optimal long-term operation strategy of the power station is calculated, which is used to evaluate the long-term stability and airtightness of the underground reservoir of the compressed gas energy storage power station.

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