Method and system for salt cavern type compressed air energy storage power station to participate in peak regulation of power grid

Abstract

The invention relates to the technical field of novel energy storage control, and provides a method and system for a salt cavern type compressed air energy storage power station to participate in peak regulation of a power grid. The method comprises the steps of obtaining power grid load data, electricity price data and energy storage power station parameters, analyzing power grid load characteristics, and determining a peak period and a valley period; establishing an optimization model under the constraint of a salt cavern energy storage state and a salt cavern pressure-capacity nonlinear relationship by taking maximum economic benefits and minimum load fluctuation as objective functions; and in the peak period and the valley period, based on newest power grid load data, electricity price data and energy storage power station parameters, a sliding time window method and a branch strategy are adopted, and a peak regulation strategy is obtained through rolling optimization iteration and optimization model solving. According to the method, efficient, flexible and economical operation of power grid peak regulation is realized by optimizing the peak regulation strategy, establishing an accurate mathematical model and a calculation method, designing a reasonable charging and discharging strategy and combining actual application examples.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy storage control, and particularly to a method and system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] With the continuous development of the power system, especially the large-scale access of renewable energy sources (such as wind power and photovoltaic power), the volatility of the power grid load has increased significantly, and the peak shaving problem has become increasingly prominent. Traditional peak shaving means mainly rely on the start-stop or variable load operation of thermal power units, but this method has problems such as slow response speed, poor flexibility, and environmental pollution.

[0004] As a large-scale energy storage technology, a salt cavern compressed air energy storage power station (Compressed-Air Energy Storage, CAES) uses an underground salt cavern as an energy storage container, converts electric energy into compressed air potential energy for storage during the low load period of the power grid, and releases compressed air to drive a generator to generate electricity during the peak load period. It has the advantages of large energy storage capacity, low cost, and long service life. However, the existing CAES peak shaving methods have the following deficiencies: First, there is a lack of an optimization strategy combined with the real-time load and electricity price fluctuations of the power grid; second, the load forecasting and electricity price forecasting data are not fully utilized, resulting in slow peak shaving response speed and low benefits; third, there is a lack of refined control of the charge and discharge process, making it difficult to maximize the stable operation of the system and economic benefits. Summary of the Invention

[0005] In order to solve the technical problems existing in the above background art, the present invention provides a method and system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving. The present invention realizes the efficient, flexible, and economic operation of power grid peak shaving by optimizing the peak shaving strategy, establishing an accurate mathematical model and calculation method, designing a reasonable charge and discharge strategy, and combining with practical application examples.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides a method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving.

[0008] A method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving includes:

[0009] Obtain power grid load data, electricity price data, and energy storage power station parameters, analyze the power grid load characteristics, and determine the peak period and the low valley period;

[0010] Taking the maximization of economic benefits and the minimization of load fluctuations as the objective function, an optimization model is established under the constraints of the salt cavern energy storage state and the non-linear relationship between salt cavern pressure and capacity;

[0011] During peak and off-peak hours, based on the latest grid load data, electricity price data, and energy storage power station parameters, the sliding time window method and the branch strategy are adopted, and through rolling optimization iteration, the optimization model is solved to obtain the peak shaving strategy.

[0012] Furthermore, the objective function is expressed by the following formula:

[0013]

[0014] where λ t is the real-time electricity price, P charge,t and P discharge,t are the charging power and discharging power at time t respectively, L t is the grid load, is the smoothed load, is the weight coefficient.

[0015] Furthermore, the salt cavern pressure:

[0016] p min ≤ p t ≤ p max

[0017] where p t is the salt cavern pressure, p min and p max are the minimum and maximum pressures allowed in the salt cavern respectively.

[0018] Furthermore, the power limit:

[0019] P min ≤ P t ≤ P max

[0020] where P t is the power of the energy storage power station at time t, P min and P max are the minimum and maximum powers allowed in the energy storage power station respectively.

[0021] Furthermore, the salt cavern energy storage state is expressed by the following formula:

[0022]

[0023] where E t is the salt cavern energy capacity at time t, E t+1 is the salt cavern energy capacity at time t + 1, η c is the charging efficiency, ηd is the discharge efficiency.

[0024] Further, the non-linear relationship between the pressure and volume of the salt cavern is expressed by the following formula:

[0025]

[0026] where p t is the pressure of the salt cavern, and k1 and k2 are geological parameters.

[0027] Further, the process of adopting the sliding time window method and the branching strategy includes:

[0028] Divide the time of one day according to the set time interval to obtain several time periods;

[0029] Define the charging state as 0 and the discharging state as 1 to construct a binary variable set;

[0030] Adopt the sliding time window method to optimize the scheduling plan for continuous N time periods, and only execute the result of the first time period. Refresh the optimization window according to the latest grid load, electricity price and salt cavern state at every set time interval;

[0031] Give priority to branching the binary variable set in the time period with the largest electricity price fluctuation to accelerate the search for feasible solutions; if the objective function value of the current node is less than the set upper bound threshold of the feasible solution, then prune; traverse all nodes to obtain the upper bound of the objective function;

[0032] Relax the binary variable set into a continuous variable interval, perform piecewise linear optimization on the non-linear relationship between the pressure and volume of the salt cavern, and construct a convex quadratic programming problem based on the load deviation term in the objective function;

[0033] Use the interior point method to solve the convex quadratic programming problem to obtain the lower bound of the objective function;

[0034] Based on the upper and lower bounds of the objective function and combined with the continuous variable interval, obtain a set of relaxation solutions; if the elements in the set of relaxation solutions do not belong to the binary variable set, then use the rounding method to generate candidate solutions.

[0035] Further, the process of using the rounding method to generate candidate solutions includes:

[0036] If the element is greater than or equal to the first threshold, set it to 1, that is, the discharging state;

[0037] If the element is less than or equal to the second threshold, set it to 0, that is, the charging state;

[0038] Add the remaining elements to the branch queue to generate child nodes.

[0039] Further, the rolling optimization iteration method includes: obtaining the current salt cavern pressure, energy storage capacity, power grid load forecast, and real-time electricity price, and setting the rolling window length;

[0040] Obtaining the load forecast result and electricity price forecast result for a period of time in the future, and calculating the initial salt cavern energy storage capacity based on the current salt cavern pressure according to the non-linear relationship between salt cavern pressure and capacity;

[0041] Based on the initial salt cavern energy storage capacity, adopting a branching strategy to solve the optimization problem within the rolling window, obtaining the optimal charge and discharge plan, and recording the peak shaving strategy for the first time period;

[0042] According to the current salt cavern pressure and the peak shaving strategy for the first time period, rolling and updating the salt cavern pressure for the next time period.

[0043] Further, the peak shaving strategy includes a time-of-use peak shaving strategy. The time-of-use peak shaving strategy divides a day into peak hours, valley hours, and flat hours according to the power grid load characteristics. During valley hours, the excess electric energy of the power grid is used to drive a compressor to compress and store air in the salt cavern; during peak hours, the compressed air in the salt cavern is released to drive a generator to generate electricity to meet the power grid load demand.

[0044] Further, the peak shaving strategy further includes an electricity price-driven strategy. The electricity price-driven strategy optimizes the charge and discharge time according to the real-time electricity price data.

[0045] Further, the peak shaving strategy further includes a charging strategy and a discharging strategy. The charging strategy is: during the charging stage, the excess electric energy of the power grid is used to drive a compressor to compress and store air in the salt cavern; the discharging strategy is: during the discharging stage, the compressed air in the salt cavern is released and heated to drive a turbogenerator to generate electricity.

[0046] Further, according to the charging strategy, during the charging stage, the dynamic adjustment of the charging power is achieved by controlling the rotation speed and flow rate of the compressor; according to the discharging strategy, during the discharging stage, the dynamic control of the discharging power is achieved by adjusting the rotation speed and air flow rate of the turbogenerator.

[0047] The second aspect of the present invention provides a system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving.

[0048] A system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving includes:

[0049] A data acquisition module, which is configured to: acquire power grid load data, electricity price data, and energy storage power station parameters, analyze the power grid load characteristics, and determine peak hours and valley hours;

[0050] A model construction module, which is configured to: establish an optimization model with maximizing economic benefits and minimizing load fluctuations as the objective function, under the constraints of the salt cavern energy storage state and the non-linear relationship between salt cavern pressure and capacity;

[0051] A solution module, which is configured to: during peak hours and off-peak hours, based on the latest power grid load data, electricity price data, and energy storage power station parameters, adopt the sliding time window method and the branch strategy, and solve the optimization model through rolling optimization iteration to obtain a peak shaving strategy.

[0052] The third aspect of the present invention provides a computer device, which includes:

[0053] A processor, adapted to execute a computer program;

[0054] A computer-readable storage medium, in which a computer program is stored. When the computer program is executed by the processor, the steps in the method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving described in the first aspect above are implemented.

[0055] The fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program. The computer program is adapted to be loaded and executed by a processor to perform the steps in the method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving described in the first aspect above.

[0056] The fifth aspect of the present invention provides a computer program product or a computer program.

[0057] The present invention provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving described in the first aspect above.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] The present invention provides a method and a system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving. With maximizing economic benefits and minimizing load fluctuations as the objective function, an optimization model is established under the constraints of the salt cavern energy storage state and the non-linear relationship between salt cavern pressure and capacity; combined with the latest power grid load data, electricity price data, and energy storage power station parameters, the optimization model is solved to obtain a peak shaving strategy, so as to achieve refined control of the charging and discharging process and maximize the stable operation of the system and economic benefits.

[0060] The present invention calculates the initial salt cavern energy storage capacity by obtaining the load prediction results and electricity price prediction results for a future period of time, based on the current salt cavern pressure and according to the non-linear relationship between salt cavern pressure and capacity; based on the initial salt cavern energy storage capacity, a branch strategy is adopted to solve the optimization problem within the rolling window, so as to obtain the optimal charge and discharge plan and peak shaving strategy, making full use of the load prediction and electricity price prediction data, improving the peak shaving response speed, and further increasing the revenue. Description of the Drawings

[0061] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0062] Figure 1 is a flowchart of the method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving shown in the embodiments of the present invention;

[0063] Figure 2 is a structural diagram of the system for a salt cavern compressed air energy storage power station to participate in power grid peak shaving shown in the embodiments of the present invention;

[0064] Figure 3 is a structural diagram of the computer device shown in the embodiments of the present invention. Detailed Embodiments

[0065] The present invention will be further described below in conjunction with the drawings and embodiments.

[0066] It should be noted that the following detailed descriptions are all illustrative and are intended to provide a further description of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0067] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or their combinations.

[0068] Figure 1 is a flowchart of the method for a salt cavern compressed air energy storage power station to participate in power grid peak shaving shown in the embodiments of the present invention. Referring to Figure 1 , the method described in the present invention includes:

[0069] Obtain power grid load data, electricity price data, and energy storage power station parameters, analyze the power grid load characteristics, and determine the peak period and valley period;

[0070] Taking the maximization of economic benefits and the minimization of load fluctuations as the objective function, an optimization model is established under the constraints of the salt cavern energy storage state and the non-linear relationship between salt cavern pressure and capacity;

[0071] During peak hours and off-peak hours, based on the latest grid load data, electricity price data, and energy storage power station parameters, the sliding time window method and the branch strategy are adopted, and through rolling optimization iteration, the optimization model is solved to obtain the peak shaving strategy.

[0072] The present invention provides a method and system for a salt cavern compressed air energy storage power station to participate in grid peak shaving. Taking the maximization of economic benefits and the minimization of load fluctuations as the objective function, an optimization model is established under the constraints of the salt cavern energy storage state and the non-linear relationship between salt cavern pressure and capacity; combined with the latest grid load data, electricity price data, and energy storage power station parameters, the optimization model is solved to obtain the peak shaving strategy, so as to achieve refined control of the charging and discharging process and maximize the stable operation of the system and economic benefits.

[0073] In some embodiments, the grid load data, electricity price data, and energy storage power station parameters are obtained, the grid load characteristics are analyzed, and the peak hours, off-peak hours, and flat hours are determined;

[0074] In some embodiments, taking the maximization of economic benefits and the minimization of load fluctuations as the objective function, an optimization model is established under the constraints of salt cavern pressure, power limit, salt cavern energy storage state, and the non-linear relationship between salt cavern pressure and capacity; specifically:

[0075] Taking the maximization of economic benefits and the minimization of load fluctuations as the objective function, the constraint conditions include salt cavern pressure, power limit, energy storage state equation, and the non-linear relationship between salt cavern pressure and capacity, etc.

[0076] The objective function is:

[0077]

[0078] where λ t is the real-time electricity price (yuan), P charge,t and P discharge,t are the charging and discharging powers (MW) at time t respectively, L t is the grid load (MW), is the smoothed load (MW), is the weight coefficient.

[0079] The constraint conditions include salt cavern pressure, power limit, energy storage state equation, and the non-linear relationship between salt cavern pressure and capacity.

[0080] Salt cavern pressure:

[0081] p min ≤ p t ≤ pmax

[0082] Power limit:

[0083] P min ≤ P t ≤ P max

[0084] Equation of state for salt cavern energy storage:

[0085]

[0086] Nonlinear relationship between salt cavern pressure and volume:

[0087]

[0088] Wherein, P t is the power of the energy storage power station at time t (MW), P min , P max are respectively the minimum and maximum powers (MW) allowed for the energy storage power station, E t+1 , E t are respectively the salt cavern energy capacities (MJ) at time t + 1 and t, p t is the salt cavern pressure (MPa), p min , p max are respectively the minimum and maximum pressures (MPa) allowed for the salt cavern, k1 and k2 are geological parameters (MJ / MPa, MJ / MPa 2 ), η c is the charging efficiency, η d is the discharging efficiency, and Δt represents the time interval (s) between time t + 1 and time t.

[0089] In some embodiments, during peak hours and off-peak hours, based on the latest grid load data, electricity price data, and energy storage power station parameters, the sliding time window method and the branch strategy are adopted, and through rolling optimization iteration, the optimization model is solved to obtain the peak shaving strategy; specifically:

[0090] The improved branch and bound is adopted, combined with the rolling horizon optimization (RHO), and the scheduling plan is updated every 15 minutes; the 24-hour scheduling plan is divided into 96 15-minute time periods.

[0091] Decision variables: binary variable u t ∈ {0, 1} (1 = discharging, 0 = charging), continuous variables: Pcharge,t and Pdischarge,t.

[0092] Construct a double objective of economy and stability, and transform it into a single objective through weighted summation:

[0093]

[0094] The constraints include the pressure in the salt cavern, power limit, energy storage state equation, and the non-linear relationship between salt cavern pressure and capacity.

[0095] The sliding time window method is adopted. Each time, the scheduling plan for the next 4 hours (16 time periods) is optimized, and only the results of the first 15 minutes are executed; every 15 minutes, the optimization window is refreshed according to the latest grid load, electricity price, and salt cavern state.

[0096] Specifically, the branch strategy is improved: the binary variable u in the time period with the largest electricity price fluctuation is preferentially branched (such as peak / low valley periods) to accelerate the search for feasible solutions; if the lower bound of the objective function value of the current node is less than the upper bound of the known feasible solution - threshold ∈, pruning is performed (∈ shrinks adaptively with the number of iterations). t Initial relaxation and convex approximation: The binary variable u is relaxed to a continuous variable u ∈ [0, 1], allowing it to take any value between 0 and 1. At this time, u can be regarded as the "discharge tendency". For example, u = 0.7 means a 70% tendency to discharge and a 30% tendency to charge; however, the actual power still needs to be constrained by the continuous variables P and P; the salt cavern pressure - capacity equation is piecewise linearized, and within the pressure interval [p, p], piecewise linear approximation is performed, with each segment having a length of 1 MPa; quadratic programming transformation: The load deviation term in the objective function is expanded to construct a convex quadratic programming (QP) problem.

[0097] Initial relaxation and convex approximation: The binary variable u t is relaxed to a continuous variable u t ∈[0, 1], allowing it to take any value between 0 and 1. At this time, u t can be regarded as the "discharge tendency". For example, u t = 0.7 means a 70% tendency to discharge and a 30% tendency to charge; however, the actual power still needs to be constrained by the continuous variables P charge,t and P discharge,t constraints; the salt cavern pressure - capacity equation is piecewise linearized, and for in the pressure interval [p min , p max , piecewise linear approximation is performed, with each segment having a length of 1 MPa; quadratic programming transformation: The load deviation term in the objective function is expanded to construct a convex quadratic programming (QP) problem. is expanded to construct a convex quadratic programming (QP) problem.

[0098] The interior point method is used to solve the relaxed QP problem to obtain the lower bound.

[0099] In some embodiments, if there are in the relaxed solution, the rounding method is adopted to generate candidate solutions:

[0100] If u t ≥ 0.8 (here 0.8 is the first threshold), it is forced to be set to 1 (discharge); if u t ≤ 0.2 (here 0.2 is the second threshold), it is forced to be set to 0 (charge). The remaining variables are added to the branch queue to generate child nodes.

[0101] In some embodiments, rolling optimization iteration is performed, specifically:

[0102] First, the current salt cavern pressure P0, energy storage capacity E0, grid load prediction {Lt}, and real-time electricity price {λt} are input; the rolling window length N = 16 (4 hours) is set, and the current time period k = 0.

[0103] Secondly, obtain the load forecast L for the next 4 hours k:k+N-1 and the electricity price forecast λ k:k+N-1 ; according to the current salt cavern pressure p k , calculate the initial energy storage capacity

[0104] Call the improved branch and bound method to solve the optimization problem within the window, and obtain the optimal charge and discharge plan {u k , P charge,k , P discharge,k}; record the decision result of the first time period

[0105] Finally, update the salt cavern pressure according to the execution result by rolling:

[0106]

[0107] Sliding window: k → k + 1, repeat until k = 96 (24 hours).

[0108] Use the optimal solution of the previous window as the initial guess to reduce the number of iterations; solve the relaxation problem for multiple branch nodes simultaneously; store the optimal solutions under common electricity price - load patterns to quickly match similar scenarios. By coordinating discrete decisions (charge and discharge states) with continuous variables (power, pressure), global optimization is achieved while ensuring real - time performance, solving the problems of low computational efficiency and easy violation of salt cavern safety constraints in traditional methods.

[0109] In some embodiments, the peak shaving strategy includes a time - of - use peak shaving strategy, an electricity price - driven strategy, and a load forecasting and dynamic adjustment strategy. The time - of - use peak shaving strategy divides a day into peak hours, valley hours, and flat hours according to the grid load characteristics. During valley hours, the excess electric energy of the grid is used to drive a compressor to compress air and store it in the salt cavern; during peak hours, the compressed air in the salt cavern is released to drive a generator to generate electricity to meet the grid load demand. The electricity price - driven strategy combines real - time electricity price data to optimize the charge and discharge time. Charge during periods with lower electricity prices (such as night valley hours) and discharge during periods with higher electricity prices (such as day peak hours) to maximize economic benefits. The load forecasting and dynamic adjustment strategy uses a grid load forecasting model and an electricity price forecasting model to plan the charge and discharge plan in advance. According to the load forecasting results, dynamically adjust the charge and discharge power of the energy storage power station to ensure the flexibility and real - time performance of the peak shaving strategy.

[0110] The peak shaving strategy further includes a charge - discharge strategy, and the charge - discharge strategy includes a charging strategy and a discharging strategy.

[0111] The charging strategy means that during the charging stage, the excess electric energy of the grid is used to drive a compressor to compress air and store it in the salt cavern. The charging power Pcharge The calculation formula is as follows:

[0112]

[0113] Among them, Q in is the compressed air flow rate, and P in is the compressed air pressure. By controlling the rotational speed and flow rate of the compressor, the charging rate is adjusted to ensure the stable operation of the system.

[0114] The discharge strategy refers to releasing the compressed air in the salt cavern during the discharge stage, heating it and then driving the turbogenerator to generate electricity. The discharge power P discharge The calculation formula is as follows:

[0115]

[0116] Among them, Q out is the released air flow rate, and P out is the released air pressure. By adjusting the rotational speed and air flow rate of the generator on the expander side, the dynamic control of the discharge power is achieved to meet the real-time load demand of the power grid.

[0117] In some embodiments, according to the charge-discharge strategy, charge-discharge control is achieved; specifically:

[0118] The charge-discharge control refers to, according to the charge-discharge strategy, during the charging stage, by controlling the rotational speed and flow rate of the compressor, the dynamic adjustment of the charging power is achieved; during the discharge stage, by adjusting the rotational speed and air flow rate of the turbogenerator, the dynamic control of the discharge power is achieved.

[0119] In some embodiments, the operating state of the energy storage power station can be monitored using real-time data, and according to the power grid load change and electricity price fluctuation, the charge-discharge strategy is dynamically adjusted to ensure the stable operation of the system.

[0120] In some embodiments, by comparing the peak-valley difference of the power grid, the peak regulation cost, and the revenue of the energy storage power station before and after application, the evaluation effect is obtained.

[0121] To better understand the present invention, an example is given below for illustration:

[0122] Suppose the load characteristics of the power grid in a certain area are as follows: The peak hours are 10:00 - 14:00 and 18:00 - 22:00, and the valley hours are 00:00 - 04:00 and 06:00 - 08:00. The peak-hour load is 1000 MW, and the valley-hour load is 400 MW.

[0123] The parameters of the energy storage power station include: the salt cavern volume E = 100000 m 3 ; the compressed air pressure p = 10 MPa; the energy storage efficiency η = 0.7; the charging efficiency η charge= 0.8; Discharge efficiency η discharge = 0.75;

[0124] (1) Peak shaving strategy implementation:

[0125] During the low - valley period (00:00 - 04:00), the energy storage power station charges at the maximum charging power P charge = 200MW for 4 hours, and the energy storage capacity reaches the upper limit.

[0126] During the peak period (10:00 - 14:00), the energy storage power station discharges at the maximum discharging power P discharge = 300MW for 3 hours, meeting the grid peak - shaving demand.

[0127] (2) Economic benefit analysis

[0128] By participating in grid peak - shaving, the energy storage power station can obtain 10 million yuan of peak - shaving compensation fees annually. At the same time, it reduces the grid peak - shaving cost and improves the grid operation efficiency.

[0129] Charging during the low - electricity - price period and discharging during the high - electricity - price period, the operation cost of the energy storage power station is reduced and the revenue is significantly increased.

[0130] (3) Peak - shaving effect evaluation

[0131] After applying the method of the present invention, the peak - valley difference of the grid is reduced from 600MW to 300MW, and the peak - shaving effect is remarkable.

[0132] The grid operation stability is improved, the utilization rate of renewable energy is increased, and the overall economic and social benefits are significantly enhanced.

[0133] Considering the grid load fluctuation, electricity - price fluctuation and energy - storage characteristics comprehensively, the present invention proposes an optimized peak - shaving strategy, which significantly improves the peak - shaving efficiency and economic benefits. An optimization model of the salt - cavern compressed - air energy - storage power station is established. Combining with real - time data and prediction models, dynamic peak - shaving control is realized. A refined charge - discharge control strategy is designed to ensure the stable operation of the system while responding to the real - time grid demand. The feasibility and superiority of the method of the present invention are verified through application examples, showing remarkable peak - shaving effects and economic benefits.

[0134] The above combination Figure 1 has introduced in detail the method of the salt - cavern compressed - air energy - storage power station participating in grid peak - shaving provided by the embodiments of the present invention. Next, the system of the salt - cavern compressed - air energy - storage power station participating in grid peak - shaving provided by the embodiments of the present invention will be introduced with reference to the drawings.

[0135] Figure 2 is the structural schematic diagram of the system of the salt - cavern compressed - air energy - storage power station participating in grid peak - shaving shown in the embodiments of the present invention. Refer toFigure 2 , the system of the present invention includes:

[0136] A data acquisition module, which is configured to: acquire power grid load data, electricity price data, and energy storage power station parameters, analyze the power grid load characteristics, and determine peak hours and valley hours;

[0137] A model construction module, which is configured to: establish an optimization model with maximizing economic benefits and minimizing load fluctuations as the objective function, under the constraints of the salt cavern energy storage state and the salt cavern pressure-capacity nonlinear relationship;

[0138] A solution module, which is configured to: during peak hours and valley hours, based on the latest power grid load data, electricity price data, and energy storage power station parameters, adopt the sliding time window method and the branch strategy, and solve the optimization model through rolling optimization iteration to obtain a peak shaving strategy.

[0139] In some embodiments, the objective function is represented by the following formula:

[0140]

[0141] where λ t is the real-time electricity price, P charge,t and P discharge,t are the charging power and discharging power at time t respectively, L t is the power grid load, is the smoothed load, is the weight coefficient;

[0142] In some embodiments, the salt cavern energy storage state is represented by the following formula:

[0143]

[0144] where E t is the salt cavern capacity at time t, E t+1 is the salt cavern capacity at time t + 1, η c is the charging efficiency, η d is the discharging efficiency.

[0145] In some embodiments, the salt cavern pressure-capacity nonlinear relationship is represented by the following formula:

[0146]

[0147] where p t is the salt cavern pressure, and k1, k2 are geological parameters.

[0148] In some embodiments, the process of adopting the sliding time window method and the branch strategy includes:

[0149] Divide a day into several time periods according to the set time interval.

[0150] Define the charging state as 0 and the discharging state as 1 to construct a set of binary variables.

[0151] Adopt the sliding time window method to optimize the scheduling plan for continuous N time periods. Only execute the result of the first time period, and refresh the optimization window according to the latest grid load, electricity price and salt cavern state at every set time interval.

[0152] Prioritize the branching of the set of binary variables in the time period with the largest electricity price fluctuation to accelerate the search for feasible solutions. If the objective function value of the current node is less than the set upper bound threshold of the feasible solution, prune the branch. Traverse all nodes to obtain the upper bound of the objective function.

[0153] Relax the set of binary variables into a continuous variable interval, perform piecewise linear optimization on the non-linear relationship between salt cavern pressure and capacity, and construct a convex quadratic programming problem based on the load deviation term in the objective function.

[0154] Use the interior point method to solve the convex quadratic programming problem to obtain the lower bound of the objective function.

[0155] Based on the upper and lower bounds of the objective function and combined with the continuous variable interval, obtain a set of relaxed solutions. If the elements in the set of relaxed solutions do not belong to the set of binary variables, use the rounding method to generate candidate solutions.

[0156] In some embodiments, the process of generating candidate solutions using the rounding method includes:

[0157] If the element is greater than or equal to the first threshold, set it to 1, that is, the discharging state.

[0158] If the element is less than or equal to the second threshold, set it to 0, that is, the charging state.

[0159] Add the remaining elements to the branch queue to generate child nodes.

[0160] In some embodiments, the rolling optimization iteration method includes: obtaining the current salt cavern pressure, energy storage capacity, grid load prediction and real-time electricity price, and setting the rolling window length.

[0161] Obtain the load prediction results and electricity price prediction results for a future period of time, and calculate the initial salt cavern energy storage capacity based on the current salt cavern pressure according to the non-linear relationship between salt cavern pressure and capacity.

[0162] Based on the initial salt cavern energy storage capacity, adopt a branching strategy to solve the optimization problem within the rolling window, obtain the optimal charging and discharging plan, and record the peak shaving strategy for the first time period.

[0163] According to the current salt cavern pressure and the peak shaving strategy in the first time period, the salt cavern pressure in the next time period is updated iteratively.

[0164] In some embodiments, the peak shaving strategy includes a time-of-use peak shaving strategy. According to the grid load characteristics, a day is divided into peak hours, valley hours, and flat hours. During valley hours, the excess electric energy of the grid is used to drive a compressor to compress and store air in the salt cavern. During peak hours, the compressed air in the salt cavern is released to drive a generator to generate electricity to meet the grid load demand.

[0165] In some embodiments, the peak shaving strategy further includes a price-driven strategy, which optimizes the charging and discharging time according to real-time electricity price data.

[0166] In some embodiments, the peak shaving strategy further includes a charging strategy and a discharging strategy. The charging strategy is as follows: during the charging stage, the excess electric energy of the grid is used to drive a compressor to compress and store air in the salt cavern. The discharging strategy is as follows: during the discharging stage, the compressed air in the salt cavern is released, heated, and then used to drive a turbogenerator to generate electricity.

[0167] In some embodiments, according to the charging strategy, during the charging stage, the dynamic regulation of the charging power is achieved by controlling the rotation speed and flow rate of the compressor. According to the discharging strategy, during the discharging stage, the dynamic control of the discharging power is achieved by adjusting the rotation speed and air flow rate of the turbogenerator.

[0168] The system for a salt cavern compressed air energy storage power station to participate in grid peak shaving according to the embodiments of the present invention can correspond to execute the methods described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the system for a salt cavern compressed air energy storage power station to participate in grid peak shaving are respectively for implementing Figure 1 the corresponding processes of the respective methods in, and for the sake of brevity, will not be described herein again.

[0169] See Figure 3The structural diagram of the computer device shown, the computer device includes a processor, a communication interface, and a computer-readable storage medium. Among them, the processor, the communication interface, and the computer-readable storage medium can be connected through a bus or other means. Among them, the communication interface is used to receive and send data. The computer-readable storage medium can be stored in the memory of the computer device, and the computer-readable storage medium is used to store computer programs, and the computer programs include program instructions, and the processor is used to execute the program instructions stored in the computer-readable storage medium. The processor (or CPU (Central Processing Unit, central processing unit)) is the computing core and control core of the computer device, and it is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the method embodiment of the salt cavern compressed air energy storage power station participating in the power grid peak shaving.

[0170] This embodiment provides a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the computer device.

[0171] And, one or more instructions suitable for being loaded and executed by the processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory; optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0172] In one embodiment, one or more instructions are stored in the computer-readable storage medium; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the method embodiment of the salt cavern compressed air energy storage power station participating in the power grid peak shaving.

[0173] This embodiment provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in the computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the corresponding steps in the method embodiment of the salt cavern compressed air energy storage power station participating in the power grid peak shaving.

[0174] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program code.

[0175] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0176] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0178] Those of ordinary skill in the art can understand that all or part of the processes of implementing the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0179] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for a salt cavern compressed air energy storage power station to participate in power grid peak regulation, characterized in that: include: Obtain grid load data, electricity price data, and energy storage power station parameters, analyze grid load characteristics, and determine peak and off-peak hours; Taking maximizing economic benefits and minimizing load fluctuation as the objective function, an optimization model is established under the constraints of the salt cavern energy storage state and the nonlinear relationship between salt cavern pressure and capacity. During peak and off-peak periods, based on the latest grid load data, electricity price data and energy storage power station parameters, the sliding time window method and branch strategy are used to solve the optimization model through rolling optimization iteration to obtain the peak-shaving strategy.

2. The method for the salt cavern compressed air energy storage power station to participate in power grid peak regulation according to claim 1, characterized in that: The objective function is expressed by the following formula: Among them, λ t is the real-time electricity price, P charge,t and P discharge,t are the charging power and discharging power at time t, L t is the grid load, To smooth the afterload, is the weight coefficient; or, Salt cave pressure: p min ≤p t ≤p max Among them, p t is the cavern pressure, p min 、p max are the minimum and maximum pressures allowed in salt caverns, respectively; or, Power Limitation: P min ≤P t ≤P max Among them, P t is the power of the energy storage station at time t, P min , P max They are the minimum and maximum power allowed by the energy storage power station; or, The energy storage state of the salt cavern is expressed by the following formula: Among them, E t is the energy capacity of the salt cave at time t, E t+1 is the energy capacity of the salt cave at time t+1, η c is the charging efficiency, η d is the discharge efficiency; or, The nonlinear relationship between salt cavern pressure and capacity is expressed by the following formula: Among them, k1 and k2 are geological parameters.

3. The method for the salt cavern compressed air energy storage power station to participate in power grid peak regulation according to claim 1, characterized in that: The process of adopting the sliding time window method and branching strategy includes: Divide a day into set time intervals to obtain several time periods; Define the charging state as 0 and the discharging state as 1 to construct a binary variable set; The sliding time window method is used to optimize the dispatch plan for N consecutive time periods. Only the results of the first time period are executed. The optimization window is refreshed at set time intervals according to the latest grid load, electricity price and salt cavern status. Prioritize the branching of the binary variable set in the period with the largest electricity price fluctuation to accelerate the search for feasible solutions; if the objective function value of the current node is less than the set upper limit threshold of the feasible solution, prune the branch; traverse all nodes to obtain the upper limit of the objective function; The binary variable set is relaxed into a continuous variable interval, the nonlinear relationship between salt cavern pressure and capacity is piecewise optimized, and a convex quadratic programming problem is constructed based on the load deviation term in the objective function. Use the interior point method to solve the convex quadratic programming problem and obtain the lower bound of the objective function; Based on the upper bound and the lower bound of the objective function, combined with the continuous variable interval, a relaxed solution set is obtained; if an element in the relaxed solution set does not belong to the binary variable set, a rounding method is used to generate a candidate solution; or, The process of generating candidate solutions by using the rounding method includes: If the element is greater than or equal to the first threshold, it is set to 1, i.e., the discharge state; If the element is less than or equal to the second threshold, it is set to 0, that is, the charging state; The remaining elements are added to the branch queue to generate child nodes.

4. The method for the salt cavern compressed air energy storage power station to participate in power grid peak regulation according to claim 1, characterized in that: The rolling optimization iteration method comprises: Obtain current salt cavern pressure, energy storage capacity, grid load forecast, and real-time electricity price, and set the rolling window length; Obtain the load forecast results and electricity price forecast results for a period of time in the future, and calculate the initial salt cavern energy storage capacity based on the current salt cavern pressure and the nonlinear relationship between salt cavern pressure and capacity; Based on the initial salt cavern energy storage capacity, a branching strategy is adopted to solve the optimization problem within the rolling window, obtain the optimal charging and discharging plan, and record the peak load regulation strategy for the first period; According to the current salt cavern pressure and the peak load regulation strategy for the first period, the salt cavern pressure for the next period is updated in a rolling manner.

5. The method for the salt cavern compressed air energy storage power station to participate in power grid peak regulation according to claim 1, characterized in that: The peak-shaving strategy includes a time-sharing peak-shaving strategy, which divides a day into peak period, valley period and flat period according to the load characteristics of the power grid. During the valley period, the excess power of the power grid is used to drive the compressor to compress the air and store it in the salt cavern; During peak hours, the compressed air in the salt caverns is released to drive the generator to generate electricity to meet the load demand of the power grid; or, The peak load regulation strategy also includes an electricity price driven strategy, which optimizes the charging and discharging time according to real-time electricity price data; or, The peak-shaving strategy also includes a charging strategy and a discharging strategy. The charging strategy is: in the charging stage, the excess electric energy of the power grid is used to drive the compressor to compress the air and store it in the salt cavern; the discharging strategy is: in the discharging stage, the compressed air in the salt cavern is released, and the turbine generator is driven to generate electricity after heating.

6. The method for participating in power grid peak regulation of a salt cavern compressed air energy storage power station according to claim 5, characterized in that: According to the charging strategy, during the charging stage, the charging power is dynamically adjusted by controlling the speed and flow of the compressor; according to the discharging strategy, during the discharging stage, the discharge power is dynamically controlled by adjusting the speed and air flow of the turbine generator.

7. A system for a salt cavern compressed air energy storage power station to participate in power grid peak regulation, characterized in that: include: A data acquisition module is configured to: acquire grid load data, electricity price data, and energy storage power station parameters, analyze grid load characteristics, and determine peak and off-peak hours; A model building module is configured to: establish an optimization model with maximizing economic benefits and minimizing load fluctuation as objective functions under the constraints of the salt cavern energy storage state and the nonlinear relationship between salt cavern pressure and capacity; The solution module is configured to: during peak and off-peak periods, based on the latest grid load data, electricity price data and energy storage power station parameters, use a sliding time window method and a branching strategy to solve the optimization model through rolling optimization iterations to obtain a peak-shaving strategy.

8. A computer device, characterized in that: a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the steps in the method for the salt cavern compressed air energy storage power station to participate in grid peak regulation as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the steps in the method for a salt cavern compressed air energy storage power station to participate in grid peak regulation as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in the method for a salt cavern compressed air energy storage power station to participate in grid peak regulation are implemented as described in any one of claims 1 to 6.

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