Battery energy storage charging and discharging control method, system and equipment for smoothing power fluctuation of new energy collection station and medium

Abstract

The invention discloses a battery energy storage charging and discharging control method, system and device for smoothing power fluctuation of a new energy collection station and a medium, and relates to the technical field of new energy storage control, and the method comprises the steps: building a response deviation penalty model of an AGC instruction of the new energy collection station, and calculating the AGC instruction response deviation penalty through the response deviation penalty model; calculating the battery energy storage degradation cost by using the battery energy storage degradation model; linear programming is carried out on AGC instruction response deviation penalty and battery energy storage degradation cost, a rolling optimization model of battery energy storage control is constructed in combination with energy storage power constraint conditions, the rolling optimization model is solved, and a battery energy storage charging and discharging optimization result is obtained; and carrying out inverse proportion distribution on the battery energy storage total charging and discharging power. The charging and discharging power of each energy storage unit is dynamically distributed according to the SOC of each energy storage unit, the balance of the stored electric quantity of each unit in battery energy storage is ensured, and the operation efficiency of battery energy storage is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of new energy storage control technology, and more specifically, to a battery energy storage charging and discharging control method, system, equipment, and medium for smoothing power fluctuations in new energy power stations. Background Technology

[0002] Energy storage charging and discharging control focuses on the energy storage system in a renewable energy hub. Based on factors such as the hub's renewable energy power generation and the system's own state, it optimizes charging and discharging decisions, essentially managing the energy storage system's energy to achieve operational optimization. To enable the energy storage system to better play its active supporting role and achieve short-term power balance in renewable energy hubs, current research focuses on control strategies to address requirements such as smooth renewable energy output and system power balance. The main technical approaches include PI control, fuzzy logic control, and model predictive control. PI control is the most mature technology, but the determination of control parameters depends on system information. Fuzzy logic control has the advantage of representing complex mapping relationships between inputs and outputs, but its effectiveness is significantly affected by rule design. Model predictive control adapts to real-time control requirements, allowing direct design of optimization objectives and constraints, and has broad prospects.

[0003] Model selection plays a crucial role in optimized control. The rolling optimization method utilizes input information to optimize control variables, directly setting the objective function to solve for the optimal control variable and outputting it to optimize the charging and discharging behavior of the energy storage system. As the control time domain rolls forward, the control results for the entire time domain are obtained. The rolling optimization control method exhibits good tracking performance and is suitable for real-time control of energy storage systems under fluctuating renewable energy output scenarios. Rolling optimization allows for the decomposition of the global optimization solution time domain, facilitating incremental control that includes the charging and discharging behavior of energy storage at N future time points, ensuring the optimized operation of the energy storage system within the control time domain. In energy storage charging and discharging control, in addition to considering factors such as system stability and power-electricity interaction, optimizing charging and discharging behavior to ensure the energy storage system's own state and extend its lifespan are also important control objectives.

[0004] For example, patent application CN114172275B discloses an energy optimization method and system for energy storage systems based on mileage life management, which can simultaneously achieve the goal of balancing SOC and mileage of energy storage components, effectively extending service life and improving economy. However, this energy optimization method does not consider the problem of grid power fluctuation. Patent application CN116565913A discloses a recursive power allocation method for energy storage systems that considers the aging cost of energy storage units. This power method allocates weights based on the aging cost per unit of energy storage and allocates the power of energy storage units according to the weights to reduce the operating aging cost. However, this method is only applicable to grid-side energy storage regulation.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] In response to the problems in related technologies, this invention proposes a battery energy storage charging and discharging control method, system, equipment, and medium for smoothing power fluctuations in new energy power stations, so as to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention provides a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy power stations, the method comprising:

[0009] S1. Based on the operation rules of the new energy collection station, establish a response deviation penalty model for the AGC command of the new energy collection station, and use the response deviation penalty model to calculate the response deviation penalty of the AGC command.

[0010] S2. Based on battery degradation data, establish a battery energy storage degradation model and use the battery energy storage degradation model to calculate the battery energy storage degradation cost.

[0011] S3. Perform linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and construct a rolling optimization model for battery energy storage control in combination with energy storage power constraints. Solve the rolling optimization model to obtain the battery energy storage charging and discharging optimization results.

[0012] S4. Based on the battery energy storage charging and discharging optimization results, the total charging and discharging power of the battery energy storage is inversely proportionally allocated to obtain the charging and discharging power value of each battery energy storage unit.

[0013] Preferably, based on the operation rules of the new energy collection station, a response deviation penalty model for AGC commands of the new energy collection station is established, and the response deviation penalty model is used to calculate the AGC command response deviation penalty, including:

[0014] S11. Based on the predefined operation rules of the new energy collection station, obtain the changes in AGC commands and grid-connected power of the new energy collection station within a preset time period;

[0015] S12. Weighted summation of AGC command changes and grid-connected power changes of new energy collection stations, and by introducing a predefined time interval, calculate the response deviation penalty for a single time period;

[0016] S13. Based on the deviation penalty for a single time period, the response deviation penalties of AGC commands in each time period are merged to obtain the response deviation penalty of AGC commands for the new energy station.

[0017] Preferably, based on battery degradation data, a battery energy storage degradation model is established, and the battery energy storage degradation cost is calculated using the battery energy storage degradation model, including:

[0018] S21. Construct a battery energy storage marginal degradation model based on battery degradation data, and calculate the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model;

[0019] S22. Divide the battery charge-discharge cycle depth into several cycle depth segments to obtain a piecewise linear approximation function, and construct a battery cycle aging cost function based on the piecewise linear approximation function.

[0020] S23. Based on the battery cycle aging cost function, calculate the battery cycle aging cost for each cycle depth segment, and merge the battery cycle aging costs for each cycle depth segment to obtain the battery energy storage degradation cost.

[0021] Preferably, constructing a battery energy storage marginal degradation model based on battery degradation data, and calculating the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model includes:

[0022] S211. Extract historical battery degradation data from battery degradation data and calculate the battery energy storage marginal degradation function at different charge and discharge depths.

[0023] S212. Based on the factors affecting the cost of battery energy storage degradation, differentiate the marginal degradation function of battery energy storage to obtain the marginal degradation amount of battery energy storage.

[0024] S213. Calculate the battery charge-discharge cycle depth for a preset time period by combining the battery energy storage marginal degradation, the power value of the battery during continuous discharge, the battery discharge efficiency, and the maximum battery capacity.

[0025] Preferably, the energy storage power constraints include the operation constraints of the new energy collection station, the operation constraints of the energy storage system, and the grid-connected power of the new energy collection station subject to AGC constraints;

[0026] Among them, the operational constraints of the energy storage system include the charging and discharging power constraints of the battery energy storage system, the capacity constraints of the battery energy storage system, and the continuity constraints of the changes in the stored energy capacity of the battery.

[0027] Preferably, linear programming is performed on the AGC command response deviation penalty and battery energy storage degradation cost, and a rolling optimization model for battery energy storage control is constructed in conjunction with energy storage power constraints. Solving the rolling optimization model yields the following battery energy storage charge and discharge optimization results:

[0028] S31. With the goal of minimizing the operating cost of energy storage, construct optimization objective functions for AGC command response deviation penalty and battery energy storage degradation cost respectively;

[0029] S32. Based on predefined energy storage power constraints and optimization objective function, construct a rolling optimization model for battery energy storage control;

[0030] S33. Solve the rolling optimization model and issue battery energy storage control commands based on the optimal solution of the rolling optimization model to obtain the battery energy storage charging and discharging optimization results.

[0031] Preferably, the objective functions for optimizing AGC command response deviation penalty and battery energy storage degradation cost are as follows:

[0032] min J = C AGC +C B ;

[0033]

[0034] In the formula, C B Indicates the cost of battery cycle degradation; C AGC The value represents the AGC command response penalty cost within the prediction time domain; k represents the current control time; N represents the prediction time domain length; l represents the loop depth segment; L represents the loop depth region; minJ represents the minimum operating cost of energy storage; Δt represents the optimization time interval; ΔP represents the minimum operating cost of energy storage. AGC,t ΔP represents the change in AGC commands received by the new energy collection station at time t. G,t This represents the change in grid-connected power of the renewable energy collection station at time t; This represents the total charging power of the battery energy storage in segment l at time t; This represents the total discharge power of the battery energy storage in segment l at time t.

[0035] Preferably, the rolling optimization model is solved, and battery energy storage control commands are issued based on the optimal solution of the rolling optimization model to obtain the battery energy storage charging and discharging optimization results, including:

[0036] S331. Based on the battery energy storage system setting parameters, operating parameters and solution parameters, obtain the new energy output prediction data for time k+1 to k+N.

[0037] S332. Take the new energy output prediction data as input to the rolling optimization model, call the optimization solver to obtain the energy storage charging and discharging power results in the prediction domain at time k, and issue the power allocation command at time k+Δt.

[0038] S333. After completing the charging and discharging command at time k+Δt, update the energy storage charging and discharging power results in the prediction domain starting from time k+Δt, and optimize again to obtain the power allocation command to be issued at time k+2Δt.

[0039] S334. Iterate through steps S332-S333 until time k+N is reached, then stop to complete the rolling optimization in the entire time domain and obtain the battery energy storage charging and discharging optimization results in the entire time domain.

[0040] Preferably, based on the battery energy storage charge and discharge optimization results, the total charge and discharge power of the battery energy storage is inversely proportionally allocated to obtain the charge and discharge power values ​​of each battery energy storage unit, including:

[0041] S41. Based on the battery energy storage charging and discharging optimization results in the full time domain, obtain the total charging and discharging power of the battery energy storage and allocate the total charging and discharging power of the battery energy storage to each energy storage unit;

[0042] S42. Using the real-time battery state of charge of the energy storage unit as a parameter, if the real-time battery state of charge of the energy storage unit is high, the charging power is low or the discharging power is high; if the real-time battery state of charge of the energy storage unit is low, the energy storage unit's allocated charging and discharging power is the same, so as to achieve dynamic balance of the battery state of charge of each battery energy storage unit.

[0043] S43. Based on the dynamic balance of the state of charge of each battery energy storage unit after allocation, the charging and discharging power values ​​of each battery energy storage unit are obtained.

[0044] Preferably, the calculation formula for the charging and discharging power value of each battery energy storage unit includes:

[0045]

[0046] In the formula, P B The total charge and discharge power of the battery energy storage is represented by n; n represents the currently controlled energy storage unit; N represents the number of controllable energy storage units within the battery energy storage system; p Bn This indicates the charging and discharging power corresponding to energy storage unit n; soc n This indicates the SOC value corresponding to battery energy storage unit n; This represents the average SOC value of the battery energy storage unit.

[0047] According to a second aspect of the present invention, a battery energy storage charging and discharging control system for smoothing power fluctuations in new energy collection stations is provided, the system comprising:

[0048] The instruction deviation penalty analysis module is used to establish a response deviation penalty model for AGC instructions of new energy stations based on the operation rules of new energy stations, and to calculate the response deviation penalty of AGC instructions using the response deviation penalty model.

[0049] The battery degradation cost analysis module is used to establish a battery energy storage degradation model based on battery degradation data, and to calculate the battery energy storage degradation cost using the battery energy storage degradation model.

[0050] The battery energy storage control optimization module performs linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and constructs a rolling optimization model for battery energy storage control in combination with energy storage power constraints. The rolling optimization model is solved to obtain the battery energy storage charging and discharging optimization results.

[0051] The charging and discharging power allocation module is used to inversely allocate the total charging and discharging power of the battery energy storage based on the battery energy storage charging and discharging optimization results, so as to obtain the charging and discharging power value of each battery energy storage unit.

[0052] According to a third aspect of the present invention, a computer device is provided.

[0053] Preferably, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above method.

[0054] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0055] Preferably, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of the above method.

[0056] The beneficial effects of this invention are as follows:

[0057] 1. Based on traditional control strategies, this invention introduces the influence of AGC commands, considers the penalty for AGC command following deviation at the hub, and introduces battery degradation factors according to the battery energy storage degradation mechanism to establish a quantifiable battery energy storage degradation model. This transforms the complex control problem into a mathematical model. At the same time, the rolling optimization method is used to gradually increase the adjustment accuracy, reduce hub power fluctuations, thereby increasing the accuracy of AGC command following and reducing operating costs.

[0058] 2. This invention dynamically allocates the charging and discharging power of each energy storage unit based on the SOC of each unit, ensuring that the stored power of each unit in the battery energy storage remains balanced, avoiding overcharging or over-discharging of the battery, effectively improving the operating efficiency of battery energy storage, and extending its service life. Compared with traditional short-sighted strategies, the daily battery energy storage degradation and AGC command following deviation penalty costs are reduced, resulting in a significant improvement in the efficiency of new energy storage stations. At the same time, it achieves better results in SOC balancing among multiple energy storage units, which is of great significance to the stable operation of new energy storage stations.

[0059] 3. This invention utilizes the battery life degradation characteristics to establish a battery energy storage degradation model, while balancing AGC command response and charging / discharging power. It constrains the energy storage charging / discharging power based on rolling optimization, and obtains the optimal charging / discharging power value in the short time domain through linear programming. Finally, it dynamically allocates charging / discharging power according to the SOC of each energy storage unit, while taking into account both AGC command following and maximizing energy storage lifespan. This solves the problems of low AGC following accuracy, frequent battery energy storage response, reduced expected lifespan, and inconsistent internal charge of battery energy storage during long-term operation in new energy hubs. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart of a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of a battery energy storage charging and discharging control system for smoothing power fluctuations in new energy collection stations according to an embodiment of the present invention.

[0063] Figure 3 This is a flowchart illustrating the specific solution process of rolling optimization in a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations, according to an embodiment of the present invention.

[0064] Figure 4 This is a typical new energy station structure diagram containing multiple energy storage units, according to an embodiment of the present invention, for a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy stations.

[0065] Figure 5 This is a power timing curve diagram within a new energy collection station in a battery energy storage charging and discharging control method for smoothing power fluctuations in a new energy collection station according to an embodiment of the present invention.

[0066] Figure 6 This is a trend diagram of battery energy storage output and AGC command in a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to an embodiment of the present invention.

[0067] Figure 7 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0068] In the picture:

[0069] 1. Command Deviation Penalty Analysis Module; 2. Battery Degradation Cost Analysis Module; 3. Battery Energy Storage Control Optimization Module; 4. Charge / Discharge Power Allocation Module. Detailed Implementation

[0070] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0071] According to embodiments of the present invention, a battery energy storage charging and discharging control method, system, device, and medium for smoothing power fluctuations in new energy hubs are provided.

[0072] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, according to one embodiment of the present invention, a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy power stations is provided, the method comprising:

[0073] S1. Based on the operation rules of the new energy collection station, establish a response deviation penalty model for the AGC command of the new energy collection station, and use the response deviation penalty model to calculate the response deviation penalty of the AGC command.

[0074] Among them, based on the operation rules of the new energy collection station, a response deviation penalty model for AGC commands of the new energy collection station is established, and the response deviation penalty model is used to calculate the response deviation penalty of AGC commands, including:

[0075] S11. Based on the predefined operation rules of the new energy collection station, obtain the changes in AGC commands and grid-connected power of the new energy collection station within a preset time period;

[0076] S12. Weighted summation of AGC command changes and grid-connected power changes of new energy collection stations, and by introducing a predefined time interval, calculate the response deviation penalty for a single time period;

[0077] S13. Based on the deviation penalty for a single time period, the response deviation penalties of AGC commands in each time period are merged to obtain the response deviation penalty of AGC commands for the new energy station.

[0078] It should be noted that, based on the overall operation rules of the hub, a penalty model for AGC command response deviation of the new energy hub is established. The penalty model for AGC command response deviation of the new energy hub is as follows:

[0079]

[0080] In the formula, C AGC,t c represents the penalty for grid-connected power fluctuations in the prediction time domain; AGC ΔP represents the instruction response deviation penalty cost in the prediction time domain. AGC,t ΔP represents the change in AGC commands received by the new energy collection station at time t. G,t Δt represents the change in grid-connected power of the renewable energy collection station at time t; Δt represents the optimization time interval.

[0081] S2. Based on battery degradation data, establish a battery energy storage degradation model and use the battery energy storage degradation model to calculate the battery energy storage degradation cost.

[0082] Among these, a battery energy storage degradation model is established based on battery degradation data, and the cost of battery energy storage degradation is calculated using the battery energy storage degradation model, including:

[0083] S21. Construct a battery energy storage marginal degradation model based on battery degradation data, and calculate the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model.

[0084] Among them, the construction of a battery energy storage marginal degradation model based on battery degradation data, and the calculation of the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model include:

[0085] S211. Extract historical battery degradation data from battery degradation data and calculate the battery energy storage marginal degradation function at different charge and discharge depths.

[0086] S212. Based on the factors affecting the cost of battery energy storage degradation, differentiate the marginal degradation function of battery energy storage to obtain the marginal degradation amount of battery energy storage.

[0087] S213. Calculate the battery charge-discharge cycle depth for a preset time period by combining the battery energy storage marginal degradation, the power value of the battery during continuous discharge, the battery discharge efficiency, and the maximum battery capacity.

[0088] S22. Divide the battery charge-discharge cycle depth into several cycle depth segments to obtain a piecewise linear approximation function, and construct a battery cycle aging cost function based on the piecewise linear approximation function.

[0089] S23. Based on the battery cycle aging cost function, calculate the battery cycle aging cost for each cycle depth segment, and merge the battery cycle aging costs for each cycle depth segment to obtain the battery energy storage degradation cost.

[0090] It should be noted that, based on battery degradation data, the battery energy storage degradation model based on rolling optimization includes:

[0091] The formula for calculating the marginal degradation of battery energy storage is as follows:

[0092]

[0093] In the formula, θ(d) t ) represents the incremental degradation of the battery during the cycling process, d t P represents the depth of charge-discharge cycles of the battery at time t. t η represents the power value of the continuous discharge process at time t. disc E represents the battery's discharge efficiency. max This indicates the maximum battery capacity.

[0094] The energy storage state-of-the-art cost model is based on the battery cycle aging cost function c(d t The battery cycle aging cost function c(d) is constructed by dividing the charge / discharge cycle depth into L segments on average, resulting in a piecewise linear approximation function. t Its expression is:

[0095]

[0096] Among them, the cycle aging cost function c of each battery segment l The expression:

[0097]

[0098] The cost of battery cycle degradation can be expressed as the sum of the costs of each segment:

[0099]

[0100] In the formula, l represents the cycle depth segment; L represents the number of cycle depth segments; and T represents the total calculation time for battery degradation cost. This represents the total charging power of the battery energy storage in segment l at time t; Δt represents the total discharge power of the battery energy storage in section l at time t; Δt represents the optimization time interval. The sum of the discharge power of each cycle depth segment at time t represents the total charge and discharge power of the battery at time t. This represents the total charge and discharge power of the battery during time period t, which is the discharge power of each cycle depth segment at time t. sum.

[0101] It should be noted that the cost of cycle aging depends on the battery discharge power within each time interval. This power may extend the battery's depth of discharge by one or more segments. To model the battery cycle depth over multiple time intervals, a charge / discharge power is assigned to each cycle depth segment. In order to independently track the energy level of each segment and identify the current cycle depth, the battery degradation marginal cost curve is a convex function, and the battery always flows from the cheapest (shallowest) available cycle depth segment to a more expensive (deeper) segment.

[0102] S3. Perform linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and construct a rolling optimization model for battery energy storage control in combination with energy storage power constraints. Solve the rolling optimization model to obtain the battery energy storage charging and discharging optimization results.

[0103] The process involves linear programming to account for AGC command response deviation penalties and battery energy storage degradation costs. A rolling optimization model for battery energy storage control is then constructed based on energy storage power constraints. Solving this rolling optimization model yields the following battery energy storage charge / discharge optimization results:

[0104] S31. With the goal of minimizing the operating cost of energy storage, construct optimization objective functions for AGC command response deviation penalty and battery energy storage degradation cost, respectively.

[0105] It should be noted that, considering both the AGC command response deviation penalty and the battery energy storage degradation cost simultaneously, the resulting objective function for the hub AGC command response deviation penalty and battery cycle degradation cost is as follows:

[0106] min J = C AGC +C B ;

[0107]

[0108] In the formula, C B Indicates the cost of battery cycle degradation; C AGC The value represents the AGC command response penalty cost within the prediction time domain; k represents the current control time; N represents the prediction time domain length; l represents the loop depth segment; L represents the loop depth region; minJ represents the minimum operating cost of energy storage; Δt represents the optimization time interval; ΔP represents the minimum operating cost of energy storage. AGC,t ΔP represents the change in AGC commands received by the new energy collection station at time t. G,t This represents the change in grid-connected power of the renewable energy collection station at time t; This represents the total charging power of the battery energy storage in segment l at time t; This represents the total discharge power of the battery energy storage in segment l at time t.

[0109] S32. Based on predefined energy storage power constraints and optimization objective function, construct a rolling optimization model for battery energy storage control.

[0110] Among them, the energy storage power constraints include the operation constraints of new energy collection stations, the operation constraints of energy storage systems, and the grid-connected power of new energy collection stations subject to AGC constraints;

[0111] The operational constraints of energy storage systems include the charging and discharging power constraints of battery energy storage systems, the capacity constraints of battery energy storage systems, and the continuity constraints of changes in the amount of electricity stored in battery energy storage systems.

[0112] It should be noted that the operational constraints of new energy power stations are mainly those that maintain the active power balance of the stations:

[0113] P G,t +P B,t =P N,t ;

[0114] In the formula, P G,t P represents the grid-connected power value of the collection station at time t. B,t P represents the power stored in the battery at time t. N,t This represents the active power generated by new energy sources within the station at time t.

[0115] The operating constraints of the energy storage system are as follows:

[0116] 0≤U Bch,t +U Bdisc,t ≤1;

[0117] In the formula, U Bch,t U represents a 0-1 variable indicating the charging state of the batteries in the station at time t. Bdisc,t The variable represents the 0-1 state of the battery discharge at time t. Both variables cannot be equal to 1 at the same time, meaning that the battery energy storage is not allowed to be charged and discharged simultaneously.

[0118] The charging and discharging power constraints of the battery energy storage system are as follows:

[0119]

[0120]

[0121] In the formula, P B,t This represents the power of the battery stored at time t. This represents the charging power of the battery at time t. This represents the discharge power of the battery at time t. This represents the charging power of the battery energy storage in the l-th cycle depth segment at time t. P represents the discharge power of the battery energy stored in the l-th cycle depth segment at time t. Bmax U represents the upper limit of battery energy storage capacity. Bch,t U represents a 0-1 variable indicating the charging state of the batteries in the station at time t. Bdisc,t This represents a 0-1 variable indicating the discharge state of the batteries in the station at time t.

[0122] The capacity constraints of the battery energy storage system are as follows:

[0123] E min ≤E B,t ≤E max ;

[0124] In the formula, E B,t E represents the battery's energy storage capacity at time t. max E represents the maximum capacity of the battery storage system during safe operation at time t. min This represents the minimum capacity required for safe operation of the battery energy storage at time t.

[0125] The continuity constraint for changes in the stored capacity of battery energy storage is as follows:

[0126]

[0127] In the formula, η ch Indicates battery energy storage and charging efficiency. This indicates the battery's energy storage and discharge efficiency.

[0128] The upper and lower limits of the grid-connected power of the collection station are constrained as follows:

[0129] P Gmin ≤P G,t ≤P Gmax ;

[0130] In the formula, P G,t P represents the grid-connected power value of the collection station at time t. Gmin Indicates the lower limit of grid-connected power, P Gmax This indicates the upper limit of grid-connected power.

[0131] The grid-connected power of the collection station is constrained by AGC as follows:

[0132] 0≤P G,t ≤P AGC,t ;

[0133] In the formula, P G,t P represents the grid-connected power value of the collection station at time t. AGC,t This represents the grid-connected power value corresponding to the AGC signal at time t.

[0134] S33. Solve the rolling optimization model and issue battery energy storage control commands based on the optimal solution of the rolling optimization model to obtain the battery energy storage charging and discharging optimization results.

[0135] The process involves solving the rolling optimization model and issuing battery energy storage control commands based on the optimal solution of the rolling optimization model. The resulting battery energy storage charging and discharging optimization results include:

[0136] S331. Based on the battery energy storage system setting parameters, operating parameters and solution parameters, obtain the new energy output prediction data for time k+1 to k+N.

[0137] S332. Take the new energy output prediction data as input to the rolling optimization model, call the optimization solver to obtain the energy storage charging and discharging power results in the prediction domain at time k, and issue the power allocation command at time k+Δt.

[0138] S333. After completing the charging and discharging command at time k+Δt, update the energy storage charging and discharging power results in the prediction domain starting from time k+Δt, and optimize again to obtain the power allocation command to be issued at time k+2Δt.

[0139] S334. Iterate through steps S332-S333 until time k+N is reached, then stop to complete the rolling optimization in the entire time domain and obtain the battery energy storage charging and discharging optimization results in the entire time domain.

[0140] It should be noted that the rolling optimization model for solving battery energy storage control, based on constraints and an optimization objective function, is mathematically represented as a linear programming problem. The rolling optimization solution yields the battery energy storage charge and discharge optimization results across the entire time domain. Figure 3 As shown, at time k, the predicted data input is obtained, and the optimization yields the energy storage charging and discharging power decision result within the prediction domain at time k. A power allocation command for time k+Δt is then selected for issuance. Upon reaching time k+Δt, the energy storage system has completed the charging and discharging command for time k+Δt. The data within the prediction domain starting from the current time k+Δt is updated, and optimization is performed again to obtain the power allocation command for time k+2Δt that needs to be issued. As the time domain advances, the rolling optimization continuously updates the energy storage charging and discharging power decision. At this point, the rolling optimization method can be used to solve the optimization model, issue corresponding control commands, and coordinate the actions of new energy power generation and battery energy storage. This process continues until time T... c The rolling optimization across the entire time domain is completed at time k+N.

[0141] S4. Based on the battery energy storage charging and discharging optimization results, the total charging and discharging power of the battery energy storage is inversely proportionally allocated to obtain the charging and discharging power value of each battery energy storage unit.

[0142] Based on the battery energy storage charge and discharge optimization results, the total charge and discharge power of the battery energy storage is inversely proportionally allocated to obtain the charge and discharge power values ​​of each battery energy storage unit, including:

[0143] S41. Based on the battery energy storage charging and discharging optimization results in the full time domain, obtain the total charging and discharging power of the battery energy storage and allocate the total charging and discharging power of the battery energy storage to each energy storage unit;

[0144] S42. Using the real-time battery state of charge of the energy storage unit as a parameter, if the real-time battery state of charge of the energy storage unit is high, the charging power is low or the discharging power is high; if the real-time battery state of charge of the energy storage unit is low, the energy storage unit's allocated charging and discharging power is the same, so as to achieve dynamic balance of the battery state of charge of each battery energy storage unit.

[0145] S43. Based on the dynamic balance of the state of charge of each battery energy storage unit after allocation, the charging and discharging power values ​​of each battery energy storage unit are obtained.

[0146] It should be noted that, as Figure 4 As shown, based on the battery energy storage charge and discharge optimization results, the real-time SOC of each energy storage unit is used as a parameter to inversely allocate the total charge and discharge power of the battery energy storage. This means that the higher the unit SOC, the lower the charging power or the higher the discharging power; when the unit SOC is the same, the allocated charge and discharge power is also the same for each unit, thereby achieving a dynamic balance of SOC among the energy storage units. The optimized charge and discharge power of each energy storage unit is as follows:

[0147]

[0148] In the formula, P B The total charge and discharge power of the battery energy storage is represented by n; n represents the currently controlled energy storage unit; N represents the number of controllable energy storage units within the battery energy storage system; p B,n The soc indicates the charging / discharging power corresponding to unit n. n This indicates the SOC value corresponding to battery energy storage unit n; This represents the average SOC (State of Charge) of the battery energy storage unit.

[0149] According to another embodiment of the invention, such as Figure 2 As shown, a battery energy storage charging and discharging control system for smoothing power fluctuations in new energy collection stations is also provided. This system includes:

[0150] The instruction deviation penalty analysis module 1 is used to establish a response deviation penalty model for AGC instructions of new energy stations based on the operation rules of new energy stations, and to calculate the response deviation penalty of AGC instructions using the response deviation penalty model.

[0151] Battery degradation cost analysis module 2 is used to establish a battery energy storage degradation model based on battery degradation data, and to calculate the battery energy storage degradation cost using the battery energy storage degradation model;

[0152] Battery energy storage control optimization module 3 performs linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and constructs a rolling optimization model for battery energy storage control in combination with energy storage power constraints. The rolling optimization model is solved to obtain the battery energy storage charging and discharging optimization results.

[0153] The charging and discharging power allocation module 4 is used to inversely allocate the total charging and discharging power of the battery energy storage based on the battery energy storage charging and discharging optimization results, so as to obtain the charging and discharging power value of each battery energy storage unit.

[0154] This invention discloses a battery energy storage charging and discharging control method for smoothing power fluctuations in new energy power stations. The effectiveness of the proposed rolling optimization method is verified through numerical examples, and the operation of the matching energy storage system in the new energy power station is analyzed. Relevant parameter settings are shown in Table 1. The photovoltaic installed capacity is 0.5MW, the wind power installed capacity is 0.7MW, the output curve is obtained by normalizing historical data, and the maximum active power capacity of the grid-side incoming line is designed to be 1.2MW. The battery energy storage capacity is 500kWh, the maximum charging and discharging power is 250kW, and the state of charge (SOC) has an upper limit of 90% and a lower limit of 10%. Other technical and economic parameters are shown in Table 1.

[0155] Table 1: Energy Storage Control Technology and Economic Parameter Setting Table

[0156]

[0157] It should be noted that in practical applications, the prediction time domain and update time scale of the optimized control model can be flexibly set according to the actual engineering needs.

[0158] Step 1: Based on the overall operation rules of the hub, establish a hub AGC command response deviation penalty model;

[0159]

[0160] In the formula, T is 36; c AGC The value is 0.16, in yuan / kWh; Δt is 5, in min.

[0161] Step 2: Establish a battery energy storage degradation model. The energy storage degradation cost model consists of the following formula:

[0162]

[0163] In the formula, L is 4; T is 36; C l The value is 2400, in yuan / kWh; Δt is 5, in min.

[0164] Step 3: Constrain the energy storage charging and discharging power, including constraints on the operation of the energy storage station, the operation of the energy storage system, and the grid connection of the energy storage station.

[0165] Step 4: Taking into account both the AGC command response deviation penalty and the battery energy storage degradation cost, the resulting objective function for the hub AGC command response deviation penalty and battery cycle degradation cost is as follows:

[0166] min J = C AGC +C B ;

[0167]

[0168] To minimize the minimum operating cost (min J) of energy storage, the energy storage degradation cost (C) must be minimized. B AGC instruction follow-up deviation penalty C AGC The sum is minimized, where C B The reduction requires a reduction in the charging power of the energy storage during the l-period. With discharge power C AGC The reduction requires a reduction in the current time period's AGC command ΔP. AGC,t Real-time power ΔP of the hub station during the current period G,t The problem can be categorized as a linear programming problem that minimizes the deviation between the energy storage charging and discharging power and the AGC (Automatic Generation Control). There exists an optimal charging and discharging power for the current time period to minimize operating costs.

[0169] Step 5: Solve based on rolling optimization to obtain the energy storage system charge and discharge optimization results in the full time domain. Use the proposed method to run the power time series curve of the new energy station under the energy storage optimization control model, as shown in the figure. Figure 5 As shown. Compared to the method proposed in this invention, the battery energy storage system in the power station, in addition to responding to AGC commands, also absorbs the power output of wind and solar energy that cannot be connected to the grid due to AGC command restrictions, thus achieving the operation goal of the new energy power station. The battery energy storage operation strategy mainly involves charging during peak wind and solar power output and when grid connection is limited, absorbing excess new energy power exceeding the upper limit of AGC commands, and discharging to support the operation of the new energy power station when output decreases and AGC command response is insufficient, thereby promoting the absorption of new energy. Since the comparative method only considers the optimal single-period, that is, it tends to fully follow AGC commands in a single period, keeping the grid-connected power at the current moment unchanged from the previous moment, this control method inevitably affects the capacity limited by battery energy storage, which may cause sudden changes in grid-connected power and overcompensation problems for new energy power fluctuations.

[0170] The trends in battery energy storage output under the proposed method and the single-moment optimization method are as follows: Figure 6 As shown in the figure, under the proposed rolling optimization method, the energy storage state of charge fluctuation is smaller and the depth of charge and discharge is lower. Compared with the single-moment optimization method, the battery energy storage repeatedly reaches the upper and lower limits, which has a significant impact on the health of the battery energy storage system.

[0171] Based on the results of the proposed rolling optimization method and the control method, the operational results, such as AGC command deviation penalty and battery energy storage degradation cost, can be statistically analyzed, as shown in Table 2. Under the control of the proposed rolling optimization method, the flexibility of battery energy storage is maximized. Compared with the short-sighted strategy, the battery energy storage degradation cost of the new energy hub is 35.95% of that in the single-moment optimization case, the command deviation penalty is 36.59% of that in the single-moment optimization case, and the total operating cost is reduced by 63.9%, indicating that the proposed operation optimization method can significantly improve the operational efficiency of the new energy hub.

[0172] Table 2: Comparison of Battery Energy Storage Operation Results between Rolling Optimization Method and Control Method for Energy Storage Control

[0173]

[0174] Step 6: Using the real-time SOC of each energy storage unit as a parameter, the charging and discharging power of the battery energy storage is inversely proportionally allocated to obtain the charging and discharging power value of each energy storage unit.

[0175] The optimized energy storage charge and discharge power of the energy storage unit is shown in the following formula:

[0176]

[0177]

[0178] In the formula, N is 10, and the initial SOC values ​​of each energy storage unit are shown in Table 3:

[0179] Table 3: Initial SOC Value of Energy Storage Unit

[0180]

[0181] After 24 hours of simulation, compared with the traditional average allocation mode, the SOC change value of the energy storage unit using the inverse proportional dynamic allocation is shown in Table 4. The standard deviation of SOC using the inverse proportional allocation is significantly reduced. This shows that under long-term operation, the inverse proportional allocation algorithm can effectively balance the SOC value of the energy storage unit, making it shift towards the SOC equilibrium side. This avoids overcharging or over-discharging of individual units due to SOC imbalance during the macro-control of the hub, thus extending the life of the energy storage unit.

[0182] Table 4: Energy Storage Unit Operating State of Charge (SOC) Value

[0183]

[0184]

[0185] Figure 7An embodiment of a computer device according to the present invention is shown. The computer device may be a server, and includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores static and dynamic information data. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiment.

[0186] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0187] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0188] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0189] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy power stations, characterized in that, The method includes: S1. Based on the operation rules of the new energy collection station, establish a response deviation penalty model for the AGC command of the new energy collection station, and use the response deviation penalty model to calculate the response deviation penalty of the AGC command. S2. Based on battery degradation data, establish a battery energy storage degradation model and use the battery energy storage degradation model to calculate the battery energy storage degradation cost. S3. Perform linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and construct a rolling optimization model for battery energy storage control in combination with energy storage power constraints. Solve the rolling optimization model to obtain the battery energy storage charging and discharging optimization results. S4. Based on the battery energy storage charging and discharging optimization results, the total charging and discharging power of the battery energy storage is inversely proportionally allocated to obtain the charging and discharging power value of each battery energy storage unit.

2. The battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 1, characterized in that, The aforementioned method establishes a response deviation penalty model for AGC commands based on the operation rules of new energy collection stations, and calculates the AGC command response deviation penalty using this model, including: S11. Based on the predefined operation rules of the new energy collection station, obtain the changes in AGC commands and grid-connected power of the new energy collection station within a preset time period; S12. Weighted summation of AGC command changes and grid-connected power changes of new energy collection stations, and by introducing a predefined time interval, calculate the response deviation penalty for a single time period; S13. Based on the deviation penalty for a single time period, the response deviation penalties of AGC commands in each time period are merged to obtain the response deviation penalty of AGC commands for the new energy station.

3. The battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 1, characterized in that, The process of establishing a battery energy storage degradation model based on battery degradation data and calculating the battery energy storage degradation cost using the model includes: S21. Construct a battery energy storage marginal degradation model based on battery degradation data, and calculate the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model; S22. Divide the battery charge-discharge cycle depth into several cycle depth segments to obtain a piecewise linear approximation function, and construct a battery cycle aging cost function based on the piecewise linear approximation function. S23. Based on the battery cycle aging cost function, calculate the battery cycle aging cost for each cycle depth segment, and merge the battery cycle aging costs for each cycle depth segment to obtain the battery energy storage degradation cost.

4. The battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 3, characterized in that, The construction of a battery energy storage marginal degradation model based on battery degradation data, and the calculation of the battery charge-discharge cycle depth based on the battery energy storage marginal degradation model, include: S211. Extract historical battery degradation data from battery degradation data and calculate the battery energy storage marginal degradation function at different charge and discharge depths. S212. Based on the factors affecting the cost of battery energy storage degradation, differentiate the marginal degradation function of battery energy storage to obtain the marginal degradation amount of battery energy storage. S213. Calculate the battery charge-discharge cycle depth for a preset time period by combining the battery energy storage marginal degradation, the power value of the battery during continuous discharge, the battery discharge efficiency, and the maximum battery capacity.

5. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 4, characterized in that, The energy storage power constraints include the operation constraints of the new energy collection station, the operation constraints of the energy storage system, and the grid-connected power of the new energy collection station subject to AGC constraints. Among them, the operational constraints of the energy storage system include the charging and discharging power constraints of the battery energy storage system, the capacity constraints of the battery energy storage system, and the continuity constraints of the changes in the stored energy capacity of the battery.

6. The battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 1, characterized in that, The linear programming of AGC command response deviation penalty and battery energy storage degradation cost, combined with the energy storage power constraint, constructs a rolling optimization model for battery energy storage control. Solving the rolling optimization model yields the following battery energy storage charge and discharge optimization results: S31. With the goal of minimizing the operating cost of energy storage, construct optimization objective functions for AGC command response deviation penalty and battery energy storage degradation cost respectively; S32. Based on predefined energy storage power constraints and optimization objective function, construct a rolling optimization model for battery energy storage control; S33. Solve the rolling optimization model and issue battery energy storage control commands based on the optimal solution of the rolling optimization model to obtain the battery energy storage charging and discharging optimization results.

7. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 6, characterized in that, The optimization objective functions for the AGC command response deviation penalty and the battery energy storage degradation cost are as follows: my J=C AGC +C B ; In the formula, C B Indicates the cost of battery cycle degradation; C AGC The value represents the AGC command response penalty cost within the prediction time domain; k represents the current control time; N represents the prediction time domain length; l represents the loop depth segment; and L represents the loop depth region. minJ represents the minimum operating cost of energy storage; Δt represents the optimization time interval; ΔP AGC,t ΔP represents the change in AGC commands received by the new energy collection station at time t. G,t This represents the change in grid-connected power of the renewable energy collection station at time t; This represents the total charging power of the battery energy storage in segment l at time t; This represents the total discharge power of the battery energy storage in segment l at time t.

8. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 7, characterized in that, The process of solving the rolling optimization model and issuing battery energy storage control commands based on the optimal solution of the rolling optimization model to obtain the battery energy storage charging and discharging optimization results includes: S331. Based on the battery energy storage system setting parameters, operating parameters and solution parameters, obtain the new energy output prediction data for time k+1 to k+N. S332. Take the new energy output prediction data as input to the rolling optimization model, call the optimization solver to obtain the energy storage charging and discharging power results in the prediction domain at time k, and issue the power allocation command at time k+Δt. S333. After completing the charging and discharging command at time k+Δt, update the energy storage charging and discharging power results in the prediction domain starting from time k+Δt, and optimize again to obtain the power allocation command to be issued at time k+2Δt. S334. Iterate through steps S332-S333 until time k+N is reached, then stop to complete the rolling optimization in the entire time domain and obtain the battery energy storage charging and discharging optimization results in the entire time domain.

9. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 1, characterized in that, Based on the battery energy storage charge and discharge optimization results, the total charge and discharge power of the battery energy storage is inversely proportionally allocated to obtain the charge and discharge power values ​​of each battery energy storage unit, including: S41. Based on the battery energy storage charging and discharging optimization results in the full time domain, obtain the total charging and discharging power of the battery energy storage and allocate the total charging and discharging power of the battery energy storage to each energy storage unit; S42. Using the real-time battery state of charge of the energy storage unit as a parameter, if the real-time battery state of charge of the energy storage unit is high, the charging power is low or the discharging power is high; if the real-time battery state of charge of the energy storage unit is low, the energy storage unit's allocated charging and discharging power is the same, so as to achieve dynamic balance of the battery state of charge of each battery energy storage unit. S43. Based on the dynamic balance of the state of charge of each battery energy storage unit after allocation, the charging and discharging power values ​​of each battery energy storage unit are obtained.

10. A battery energy storage charging and discharging control method for smoothing power fluctuations in new energy collection stations according to claim 9, characterized in that, The calculation formulas for the charging and discharging power values ​​of each battery energy storage unit include: In the formula, P B The total charge and discharge power of the battery energy storage is represented by n; n represents the currently controlled energy storage unit; N represents the number of controllable energy storage units within the battery energy storage system; p B,n This indicates the charging and discharging power corresponding to energy storage unit n; soc n This indicates the SOC value corresponding to battery energy storage unit n; This represents the average SOC (State of Charge) of the battery energy storage unit.

11. A battery energy storage charging and discharging control system for smoothing power fluctuations in new energy hubs, used to implement the battery energy storage charging and discharging control method for smoothing power fluctuations in new energy hubs as described in any one of claims 1-10, the system comprising: The instruction deviation penalty analysis module is used to establish a response deviation penalty model for AGC instructions of new energy stations based on the operation rules of new energy stations, and to calculate the response deviation penalty of AGC instructions using the response deviation penalty model. The battery degradation cost analysis module is used to establish a battery energy storage degradation model based on battery degradation data, and to calculate the battery energy storage degradation cost using the battery energy storage degradation model. The battery energy storage control optimization module performs linear programming on the AGC command response deviation penalty and battery energy storage degradation cost, and constructs a rolling optimization model for battery energy storage control in combination with energy storage power constraints. The rolling optimization model is solved to obtain the battery energy storage charging and discharging optimization results. The charging and discharging power allocation module is used to inversely allocate the total charging and discharging power of the battery energy storage based on the battery energy storage charging and discharging optimization results, so as to obtain the charging and discharging power value of each battery energy storage unit.

12. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 10.

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