Hybrid energy storage frequency modulation control method based on dynamic constraint and multi-objective optimization

Abstract

The invention discloses a hybrid energy storage frequency modulation control method based on dynamic constraint and multi-objective optimization, and belongs to the technical field of energy storage frequency modulation. Comprising the following steps: S1, acquiring a hybrid energy storage AGC frequency modulation power instruction; s2, constructing a hybrid energy storage system coordination control model based on the hybrid energy storage AGC frequency modulation power instruction, and obtaining charging power and discharging power of a flywheel energy storage system and a battery energy storage system; s3, constructing an energy storage unit charging and discharging coordinated optimization model based on the charging power and the discharging power in the S2, and obtaining the charging power and the discharging power of the energy storage unit; and S4, evaluating a coordinated optimization result based on the evaluation index to obtain a hybrid energy storage frequency modulation control scheme. According to the invention, the service life loss of the battery energy storage system is effectively reduced, the continuous operation capability of flywheel energy storage is improved, the energy loss of the system is reduced, the SOC balance between the energy storage units is maintained, and the comprehensive performance of the hybrid energy storage system participating in AGC frequency modulation is obviously improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage frequency regulation technology, specifically relating to a hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization. Background Technology

[0002] As building a new power system dominated by new energy sources becomes crucial for energy transition, the penetration rate of renewable energy sources such as wind power and photovoltaics is continuously increasing. However, the randomness and volatility of these power sources' output pose a severe challenge to the frequency stability of the power system. As the traditional mainstay of frequency regulation, thermal power units, limited by their mechanical inertia and response delay, struggle to meet the demands of rapid and frequent automatic power generation control.

[0003] Against this backdrop, energy storage systems with millisecond-level response speeds and bidirectional regulation capabilities have become a crucial technological support for improving the frequency regulation performance of power grids. Currently, research on energy storage systems assisted in frequency regulation has made some progress, but most focuses on the joint operation of battery energy storage and thermal power units. While such strategies can improve frequency regulation, they fail to fundamentally solve the inherent problem of battery energy storage's short cycle life and accelerated aging and degradation due to frequent charging and discharging.

[0004] In recent years, flywheel energy storage, with its advantages of high power density and long lifespan, has attracted attention. Combining it with battery energy storage to form a hybrid energy storage system for frequency regulation is expected to compensate for the shortcomings of single energy storage. However, how to coordinate the output of two energy storage systems with very different characteristics to achieve complementary advantages has become a current research challenge. Existing coordination control strategies often cannot simultaneously address the issues of reducing battery lifespan loss and maintaining the sustainable operation of flywheel energy storage. Furthermore, in practical engineering, energy storage systems are usually composed of multiple energy storage units, and existing research on the coordinated control between these units often only aims at balancing the state of charge, neglecting the optimization of overall system energy loss caused by differences in charging and discharging efficiency.

[0005] Therefore, existing technologies lack an effective solution to address the frequency regulation needs caused by grid frequency fluctuations, which can fully leverage the complementary advantages of hybrid energy storage and comprehensively optimize battery life, flywheel sustainability, system energy loss, and inter-unit state balance at the system level. Summary of the Invention

[0006] The purpose of this invention is to provide a hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization, which fully leverages the advantages of battery and flywheel energy storage, reduces system losses, extends battery life, and improves frequency regulation performance.

[0007] To achieve the above objectives, the present invention provides the following solution: a hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization, comprising the following steps: S1. Obtain the hybrid energy storage AGC frequency regulation power command of the thermal power-hybrid energy storage combined system; S2. Based on the hybrid energy storage AGC frequency modulation power command, construct a coordinated control model for the hybrid energy storage system to obtain the charging power and discharging power of the flywheel energy storage system and the battery energy storage system. S3. Based on the charging power and discharging power in S2, construct a coordinated optimization model for the charging and discharging of the energy storage unit to obtain the charging power and discharging power of the energy storage unit. S4. Evaluate the results of coordinated optimization based on evaluation indicators to obtain a hybrid energy storage frequency regulation control scheme.

[0008] More preferably, in S1, the hybrid energy storage AGC frequency modulation power command includes: ; In the formula, for t The frequency regulation power of the hybrid energy storage system at any given time; a positive value represents the hybrid energy storage system discharging, and a negative value represents the hybrid energy storage system charging. for t AGC instructions at any given time; for t The thermal power units are outputting power in real time.

[0009] More preferably, S2 includes the following steps: S21. Construct a dynamic adjustment coefficient for the maximum output of flywheel energy storage based on the improved Sigmoid function; S22. Based on the dynamic adjustment coefficient of the maximum output of the flywheel energy storage, the maximum charging power and maximum discharging power of the flywheel energy storage are corrected; S23. Determine the real-time available power of the energy storage system based on the real-time power of the energy storage unit; S24. Determine the charging and discharging power of the flywheel energy storage system and the battery energy storage system based on the hybrid energy storage AGC frequency modulation power command, the maximum charging power and maximum discharging power of the flywheel energy storage, and the real-time callable power of the energy storage system.

[0010] More preferably, in S21, the dynamic adjustment coefficient for the maximum output of the flywheel energy storage includes: in, ; In the formula, , They are respectively t Dynamic adjustment coefficient of maximum charging and discharging power of the flywheel energy storage system; , These are the high and low SOC warning values ​​for the flywheel energy storage system, respectively. , These represent the maximum and minimum SOC values ​​of the flywheel energy storage system, respectively. Q , b To improve the characteristic parameters of the Sigmoid function; , They are respectively Time-lapse flywheel energy storage system and flywheel energy storage unit m SOC; M This represents the number of energy storage units. This is the scheduling time step.

[0011] More preferably, in S23, the real-time available power of the energy storage system includes: ; ; In the formula, , They are respectively v Type of energy storage system in t The available charging power and available discharging power at any given time. v Represents the type of energy storage system. v =BESS stands for Battery Energy Storage System v =FESS stands for Flywheel Energy Storage System; , They are respectively v Energy storage units of various types of energy storage systems m exist t The available charging power and available discharging power at any time; , for v Energy storage units of various types of energy storage systems m exist t Available charging power and available discharging power at any given time; , They are respectively v Energy storage units of various types of energy storage systems m The maximum and minimum values ​​of SOC; for v Energy storage units of various types of energy storage systems m exist SOC value at time t; , They are respectively v Energy storage units of various types of energy storage systems m Charging efficiency and discharging efficiency; for v Energy storage units of various types of energy storage systems mRated capacity; for v Energy storage units of various types of energy storage systems m Rated power.

[0012] More preferably, in S3, the energy storage unit charge-discharge coordination optimization model includes: a system energy loss minimization optimization model and an energy storage system SOC equalization optimization model; S3 includes the following steps: S31. Construct a system energy loss minimization optimization model based on the charging and discharging power of the battery energy storage unit; S32. Construct an energy storage system SOC balance optimization model based on the SOC value of the energy storage unit; S33. Construct constraints; S34. Solve the energy storage unit charging and discharging coordination optimization model to obtain the charging and discharging power of each energy storage unit.

[0013] More preferably, the system energy loss minimization optimization model includes: ; ; In the formula, Battery energy storage unit m exist t Energy loss at any moment; Battery energy storage unit m exist t The charging and discharging power at any given moment.

[0014] More preferably, the SOC equilibrium optimization model of the energy storage system includes: ; in, ; ; ; In the formula, Battery energy storage unit m exist t SOC at any given moment; For battery energy storage units in The average SOC at time point; Battery energy storage unit m exist t The change in SOC at time t; v =BESU represents a battery energy storage unit.

[0015] More preferably, the evaluation indicators include: response deviation rate, battery energy storage system service life, active power loss rate, and energy storage system SOC balance. The response deviation rate index includes: ; In the formula, for t Frequency regulation output of hybrid energy storage system at all times; T Total scheduling duration; The service life indicators of the battery energy storage system include: ; In the formula, This represents the cycle life at a depth of discharge of 1. This represents the total equivalent cycle life within the scheduling period; The active power loss rate index includes: ; In the formula, flywheel energy storage unit m exist t Energy loss at any moment; flywheel energy storage unit m exist t The charging and discharging power at any given moment.

[0016] The SOC balance index of the energy storage system includes: .

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention fully leverages the advantages of long lifespan of flywheel energy storage systems and high energy density of battery energy storage systems. It addresses the issue of low energy density and frequent reaching of the SOC upper and lower limits, thus enhancing the sustainable operation of flywheel energy storage systems. Simultaneously, it reduces the lifespan degradation of battery energy storage systems. The energy storage unit coordinated control strategy proposed in this invention, using the model constructed in this invention, significantly reduces response deficit power and response deviation rate. The hybrid energy storage coordinated control strategy proposed in this invention avoids the significant advantages of flywheel energy storage systems in reducing energy loss and maintaining SOC balance among energy storage units. This strategy enables the SOC balance of each energy storage unit to be restored, preventing one or more energy storage units from prematurely reaching their SOC upper and lower limits, thus hindering charging and discharging and affecting frequency regulation performance. Attached Figure Description

[0018] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are 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.

[0019] Figure 1 A schematic diagram of the framework of the hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the combined thermal power and hybrid energy storage system according to an embodiment of the present invention; Figure 3 This is a graph showing the relationship between the dynamic adjustment coefficient of the maximum output of the flywheel energy storage system and the State of Charge (SOC) in an embodiment of the present invention. Figure 4 This diagram illustrates the response of the energy storage system to AGC commands under different strategies in embodiments of the present invention. Figure 5 The diagrams show the SOC variation curves of the energy storage system under different strategies in the embodiments of the present invention; wherein, (a) is a diagram showing the SOC variation curve of the energy storage system under comparative strategy 1; (b) is a diagram showing the SOC variation curve of the energy storage system under comparative strategy 2; and (c) is a diagram showing the SOC variation curve of the energy storage system under the strategy of the present invention. Figure 6 This is a comparison chart of the SOC curves of the battery energy storage system under different strategies in the embodiments of the present invention; Figure 7 The diagrams are schematic diagrams of the frequency regulation output of each energy storage unit under different strategies in the embodiments of the present invention; wherein, (a) is a schematic diagram of the frequency regulation output of each energy storage unit under comparative strategy 1; (b) is a schematic diagram of the frequency regulation output of each energy storage unit under comparative strategy 2; and (c) is a schematic diagram of the frequency regulation output of each energy storage unit under the strategy of the present invention. Figure 8 The diagrams are schematic diagrams of the SOC change curves of each energy storage unit under different strategies in the embodiments of the present invention; wherein, (a) is a schematic diagram of the SOC change curves of each energy storage unit under comparative strategy 1; (b) is a schematic diagram of the SOC change curves of each energy storage unit under comparative strategy 2; and (c) is a schematic diagram of the SOC change curves of each energy storage unit under the strategy of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Example 1: This embodiment provides a hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization, such as... Figure 1 The diagram shown is a collaborative control framework diagram of a battery-flywheel hybrid energy storage system.

[0023] S1. Obtain the hybrid energy storage AGC frequency regulation power command of the thermal power-hybrid energy storage combined system.

[0024] The dispatch center automatically generates AGC commands by monitoring real-time information such as power system frequency, load, and generator status, and then sends them to the unit DCS and hybrid energy storage control system. The unit DCS sends output commands to the thermal power units and receives real-time output and other operating statuses of the thermal power units. The hybrid energy storage control system subtracts the received AGC command from the real-time output of the thermal power units fed back from the unit DCS as the AGC command that the hybrid energy storage system needs to undertake.

[0025] Specifically, the hybrid energy storage AGC frequency modulation power command is as follows: (1) In the formula, for t The frequency regulation power of the hybrid energy storage system at any given time; a positive value represents the hybrid energy storage system discharging, and a negative value represents the hybrid energy storage system charging. for t AGC instructions at any given time; for t The thermal power units are outputting power in real time.

[0026] S2. Based on the hybrid energy storage AGC frequency modulation power command, construct a coordinated control model for the hybrid energy storage system to obtain the charging power and discharging power of the flywheel energy storage system and the battery energy storage system.

[0027] Specifically, obtaining the charging and discharging power of the flywheel energy storage system and the battery energy storage system based on the coordinated control model of the hybrid energy storage system includes the following steps: S21. Construct a dynamic adjustment coefficient for the maximum output of flywheel energy storage based on the improved Sigmoid function.

[0028] like Figure 3 The figure shown is a graph depicting the relationship between the dynamic adjustment coefficient of the maximum output of the flywheel energy storage system and the State of Charge (SOC). In this embodiment, the dynamic adjustment coefficient of the maximum output of the flywheel energy storage system is as follows: (2) (3) in, (4) In the formula, , They are respectively t Dynamic adjustment coefficient of maximum charging and discharging power of the flywheel energy storage system; , These are the high and low SOC warning values ​​for the flywheel energy storage system, respectively. , These represent the maximum and minimum SOC values ​​of the flywheel energy storage system, respectively. Q , b To improve the characteristic parameters of the Sigmoid function, this embodiment takes values ​​of 0.01 and 45 respectively; , They are respectively Time-lapse flywheel energy storage system and flywheel energy storage unit m SOC; M This represents the number of energy storage units. This is the scheduling time step.

[0029] S22. The maximum charging power and maximum discharging power of flywheel energy storage are corrected based on the dynamic adjustment coefficient of the maximum output power of flywheel energy storage.

[0030] (5) In the formula, , They are respectively t The maximum charging power and maximum discharging power of the flywheel energy storage system at any time; This is the rated power of the flywheel energy storage system.

[0031] S23. Obtain the real-time power of the energy storage unit through the SOC calculation module of the hybrid energy storage system, introduce the real-time callable power constraint of the energy storage system, and use the real-time callable power and the rated power together as the upper limit of the charging and discharging power of the energy storage system.

[0032] In this embodiment, the method for calculating the real-time available power of the energy storage system is as follows: (6) (7) In the formula, , They are respectively v Type of energy storage system in t The available charging power and available discharging power at any given time. v Represents the type of energy storage system. v =BESS stands for Battery Energy Storage System v=FESS stands for Flywheel Energy Storage System v =BESU represents a battery energy storage unit; , They are respectively v Energy storage units of various types of energy storage systems m exist t The available charging power and available discharging power at any time; , for v Energy storage units of various types of energy storage systems m exist t Available charging power and available discharging power at any given time; , They are respectively v Energy storage units of various types of energy storage systems m The maximum and minimum values ​​of SOC; for v Energy storage units of various types of energy storage systems m exist SOC value at time t; , They are respectively v Energy storage units of various types of energy storage systems m Charging efficiency and discharging efficiency; for v Energy storage units of various types of energy storage systems m Rated capacity; for v Energy storage units of various types of energy storage systems m Rated power.

[0033] S24. Determine the charging and discharging power of the flywheel energy storage system and the battery energy storage system based on the hybrid energy storage AGC frequency modulation power command, the maximum charging power and maximum discharging power of the flywheel energy storage, and the real-time callable power of the energy storage system.

[0034] (8) (9) In the formula, , Flywheel energy storage system and battery energy storage system, respectively t The charging and discharging power at any given moment; This refers to the rated power of the battery energy storage system.

[0035] S3. Based on the charging power and discharging power in S2, construct a coordinated optimization model for the charging and discharging of the energy storage unit to obtain the charging power and discharging power of the energy storage unit.

[0036] In this embodiment, the energy storage unit charging and discharging coordination optimization model includes: a system energy loss minimization optimization model and an energy storage system SOC equalization optimization model. The process of obtaining the charging power and discharging power of the energy storage unit through the energy storage unit coordination optimization model is as follows: S31. Construct a system energy loss minimization optimization model based on the charging and discharging power of the battery energy storage unit.

[0037] Because energy storage units experience losses during charging and discharging due to internal resistance and internal chemical reactions, their charging and discharging efficiency cannot reach 100%. Differences in the type of energy storage unit and the inconsistent charging and discharging characteristics of the same type also result in inconsistent charging and discharging efficiencies, leading to varying energy losses during charging and discharging.

[0038] The energy loss of the energy storage unit is specifically expressed as follows: (10) In the formula, Battery energy storage unit m exist t Energy loss at any moment; Battery energy storage unit m exist t The charging and discharging power at any given moment.

[0039] With minimizing energy loss in the energy storage system as the optimization objective, the following optimization model for minimizing system energy loss is obtained: (11) S32. Construct an energy storage system SOC balance optimization model based on the SOC value of the energy storage unit.

[0040] The bidirectional regulation capability of an energy storage unit depends on its State of Charge (SOC). If only energy loss is considered as the optimization objective, it may lead to frequent activation of energy storage units with low energy loss, while units with high energy loss remain inactive, resulting in an imbalance in the SOC of the energy storage systems and reduced operating efficiency. To maintain SOC balance across the energy storage systems, this embodiment also considers minimizing the square of the difference between the current SOC of each energy storage unit and the average SOC at the previous scheduling time as the optimization objective, including: (12) in, (13) (14) (15) In the formula, Battery energy storage unit m exist tSOC at any given moment; For battery energy storage units in The average SOC at time point; Battery energy storage unit m exist t The change in SOC at time t.

[0041] S33. Construct constraints.

[0042] (16) (17) in, (18) S34. Solve the energy storage unit charging and discharging coordination optimization model to obtain the charging and discharging power of each energy storage unit.

[0043] The energy storage unit charging and discharging coordination optimization model is a multi-objective optimization model. This embodiment uses a multi-objective genetic algorithm to solve it, comprehensively considering the frequency regulation responsibility undertaken by the energy storage system and the consistency of the SOC of each energy storage unit to construct adaptive weight coefficients, in order to find the optimal solution from the Pareto front. The specific content is as follows: First, the objective function values ​​(such as energy loss values ​​of the energy storage system and the SOC balance degree of the energy storage system) are transformed into positive indicators and normalized: (19) In the formula, for t Time of the first i Group of solutions j The normalized values ​​of the objective function; , They are respectively t Time of the first j The maximum and minimum values ​​among all function values ​​of a given objective function; for t Time of the first i Group of solutions j The objective function value.

[0044] Next, calculate the weights of each indicator: (20) ;(twenty one) in, ;(twenty two) In the formula, for t The frequency regulation responsibility undertaken by the energy storage system at any time; , These are the maximum and minimum settings for frequency regulation responsibility, respectively, reflecting the degree of importance attached to energy loss. It is the sum of the SOC of each energy storage unit and the average SOC of the energy storage units at the previous scheduling time. The smaller the value, the closer the SOC of each energy storage unit is to being consistent. For the energy storage unit at the previous scheduling time m SOC; This represents the average SOC of the energy storage units at the previous scheduling time. This is the theoretical maximum value of the sum of the SOC of each energy storage unit and the average SOC of the energy storage units.

[0045] Finally, calculate the overall score: ;(twenty three) In the formula, for t Time of the first i The overall score of the solution set; l This represents the total number of objective function values.

[0046] The optimal solution is determined by the comprehensive score of each solution in the Pareto solution set. The solution with the largest comprehensive score is selected as the optimal solution. That is, the solution with the largest comprehensive score in the Pareto solution set is the charging and discharging power of each energy storage unit.

[0047] S4. Evaluate the results of coordinated optimization based on evaluation indicators to obtain a hybrid energy storage frequency regulation control scheme.

[0048] In this embodiment, the evaluation indicators include: response deviation rate, battery energy storage system service life, active power loss rate, and energy storage system SOC balance.

[0049] The response deviation rate index includes: ;(twenty four) In the formula, for t Frequency regulation output of hybrid energy storage system at all times; T This is the total scheduling duration.

[0050] The service life indicators for battery energy storage systems include: (25) In the formula, This represents the cycle life at a depth of discharge of 1. This represents the total equivalent cycle life within the scheduling period.

[0051] Active power loss rate indicators include: (26) In the formula, flywheel energy storage unit m exist t Energy loss at any moment; flywheel energy storage unit m exist t The charging and discharging power at any given moment.

[0052] The SOC balance index of an energy storage system includes: (27) To verify the effectiveness of the method proposed in this invention, a thermal power-hybrid energy storage system was simulated in MATLAB. The frequency regulation effect, battery charge-discharge depth, energy loss of the energy storage system, and SOC balance were analyzed. To further verify the superiority of the analytical method of this invention, two different comparison strategies were set up to compare the effects of energy storage-assisted frequency regulation of thermal power units under different strategies. The comparison strategies are as follows: Comparison Strategy 1: The energy storage configuration is exactly the same as that of this invention. The hybrid energy storage charge and discharge control is controlled according to the SOC of the battery / flywheel energy storage system. Specifically, the energy storage system with the SOC in the optimal working range is prioritized to participate in power regulation. The frequency regulation power distribution between energy storage units adopts an equal proportion distribution strategy.

[0053] Comparison Strategy 2: The energy storage configuration is exactly the same as that of the present invention. The hybrid energy storage charging and discharging control adopts the control strategy proposed in the present invention, but does not consider the adaptive adjustment of the maximum output of the flywheel energy storage and the real-time callable power of the energy storage system; the frequency regulation power allocation between energy storage units is dynamically proportionally allocated according to the SOC of the energy storage units.

[0054] Figure 4 This is a diagram illustrating the response of an energy storage system to AGC commands under different strategies according to embodiments of this application, such as... Figure 4 As shown, the energy storage system in comparison strategy 2 performs the worst in following AGC commands, followed by strategy 1, while the strategy of this invention performs the best in following AGC commands.

[0055] Figure 5 These are the SOC variation curves of the energy storage system under different strategies according to the embodiments of this application, such as... Figure 5 As shown, compared to Strategy 2, which does not consider the adaptive adjustment of the maximum output of the flywheel energy storage system, the SOC of the flywheel energy storage system frequently reaches the upper and lower limits. The strategy of the present invention makes up for the shortcomings of Strategy 2, thereby avoiding the frequent reaching of the upper and lower limits of the flywheel energy storage system SOC, greatly reducing the response deviation rate and improving the sustainable operation capability of the flywheel energy storage system. This fully demonstrates the superiority of the strategy of the present invention.

[0056] Figure 6 This is a comparison chart of the SOC curves of battery energy storage systems under different strategies according to embodiments of this application. Figure 6 As shown, the battery energy storage charge-discharge depth of comparison strategy 1 is greater than that of comparison strategy 2 and the strategy of the present invention, while the charge-discharge depths of comparison strategy 2 and the strategy of the present invention are basically the same.

[0057] Figure 7 The frequency modulation output of each energy storage unit is determined according to different strategies in the embodiments of this application. Figure 7 It can be seen that Comparison Strategy 1 does not consider the different characteristics of energy storage units. The frequency regulation power of energy storage units is allocated proportionally, and each energy storage unit has the same frequency regulation output. In Comparison Strategy 1, the battery energy storage system bears more frequency regulation responsibility than the flywheel energy storage system. However, the charging and discharging efficiency of battery energy storage units is generally lower than that of flywheel energy storage units. Therefore, Comparison Strategy 1 has the largest energy loss rate. In Comparison Strategy 2, the frequency regulation power of energy storage units is dynamically allocated according to the SOC of the energy storage units. The frequency regulation output of each energy storage unit depends on its own SOC state. When the SOC state of the energy storage unit with low charging and discharging efficiency is optimal, the frequency regulation power it bears is greater, resulting in greater energy loss. The strategy of this invention optimizes with the goal of SOC balance and minimizing energy loss. The energy storage units with high charging and discharging efficiency bear more frequency regulation responsibility, resulting in less energy loss. Therefore, the active power loss rate of this invention is minimized, demonstrating the superiority of this invention in reducing energy loss.

[0058] Figure 8 These are the SOC change curves of each energy storage unit under different strategies according to the embodiments of this application, derived from... Figure 8 It can be seen that under the strategy of the present invention, the SOC of each battery energy storage unit overlaps at 140 min, and the SOC of each flywheel energy storage unit overlaps at 20 min. The overlap of the SOC of each energy storage unit is significantly better than that of comparative strategy 1 and comparative strategy 2, thus demonstrating the superiority of the strategy of the present invention.

[0059] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization, characterized in that, Includes the following steps: S1. Obtain the hybrid energy storage AGC frequency regulation power command of the thermal power-hybrid energy storage combined system; S2. Based on the hybrid energy storage AGC frequency modulation power command, construct a coordinated control model for the hybrid energy storage system to obtain the charging power and discharging power of the flywheel energy storage system and the battery energy storage system. S3. Based on the charging power and discharging power in S2, construct a coordinated optimization model for the charging and discharging of the energy storage unit to obtain the charging power and discharging power of the energy storage unit. S4. Evaluate the results of coordinated optimization based on evaluation indicators to obtain a hybrid energy storage frequency regulation control scheme.

2. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 1, characterized in that, In S1, the hybrid energy storage AGC frequency modulation power command includes: ； In the formula, for t The frequency regulation power of the hybrid energy storage system at any given time; a positive value represents the hybrid energy storage system discharging, and a negative value represents the hybrid energy storage system charging. for t AGC instructions at any given time; for t The thermal power units are outputting power in real time.

3. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 1, characterized in that, S2 includes the following steps: S21. Construct a dynamic adjustment coefficient for the maximum output of flywheel energy storage based on an improved Sigmoid function; S22. Based on the dynamic adjustment coefficient of the maximum output of the flywheel energy storage, the maximum charging power and maximum discharging power of the flywheel energy storage are corrected; S23. Determine the real-time available power of the energy storage system based on the real-time power of the energy storage unit; S24. Determine the charging and discharging power of the flywheel energy storage system and the battery energy storage system based on the hybrid energy storage AGC frequency modulation power command, the maximum charging power and maximum discharging power of the flywheel energy storage, and the real-time callable power of the energy storage system.

4. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 3, characterized in that, In S21, the dynamic adjustment coefficient for the maximum output of the flywheel energy storage includes: in, ； In the formula, , They are respectively t Dynamic adjustment coefficient of maximum charging and discharging power of the flywheel energy storage system; , These are the high and low SOC warning values ​​for the flywheel energy storage system, respectively. , These represent the maximum and minimum SOC values ​​of the flywheel energy storage system, respectively. Q , b To improve the characteristic parameters of the Sigmoid function; , They are respectively Time-lapse flywheel energy storage system and flywheel energy storage unit m SOC; M This represents the number of energy storage units. This is the scheduling time step.

5. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 3, characterized in that, In S23, the real-time available power of the energy storage system includes: ； ； In the formula, , They are respectively v Type of energy storage system in t The available charging power and available discharging power at any given time. v Represents the type of energy storage system. v =BESS stands for Battery Energy Storage System v =FESS stands for Flywheel Energy Storage System; , They are respectively v Energy storage units of various types of energy storage systems m exist t The available charging power and available discharging power at any time; , for v Energy storage units of various types of energy storage systems m exist t Available charging power and available discharging power at any given time; , They are respectively v Energy storage units of various types of energy storage systems m The maximum and minimum values ​​of SOC; for v Energy storage units of various types of energy storage systems m exist SOC value at time t; , They are respectively v Energy storage units of various types of energy storage systems m Charging efficiency and discharging efficiency; for v Energy storage units of various types of energy storage systems m Rated capacity; for v Energy storage units of various types of energy storage systems m Rated power.

6. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 1, characterized in that, In S3, the energy storage unit charging and discharging coordination optimization model includes: a system energy loss minimization optimization model and an energy storage system SOC equalization optimization model; S3 includes the following steps: S31. Construct a system energy loss minimization optimization model based on the charging and discharging power of the battery energy storage unit; S32. Construct an energy storage system SOC balance optimization model based on the SOC value of the energy storage unit; S33. Construct constraints; S34. Solve the energy storage unit charging and discharging coordination optimization model to obtain the charging and discharging power of each energy storage unit.

7. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 6, characterized in that, The system energy loss minimization optimization model includes: ； ； In the formula, Battery energy storage unit m exist t Energy loss at any moment; Battery energy storage unit m exist t The charging and discharging power at any given moment.

8. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 6, characterized in that, The SOC equalization optimization model for the energy storage system includes: ； in, ； ； ； In the formula, Battery energy storage unit m exist t SOC at any given moment; For battery energy storage units in The average SOC at time point; Battery energy storage unit m exist t The change in SOC at time t; v =BESU represents a battery energy storage unit.

9. The hybrid energy storage frequency regulation control method based on dynamic constraints and multi-objective optimization according to claim 1, characterized in that, The evaluation indicators include: response deviation rate, battery energy storage system service life, active power loss rate, and energy storage system SOC balance. The response deviation rate index includes: ； In the formula, for t Frequency regulation output of hybrid energy storage system at all times; T Total scheduling duration; The service life indicators of the battery energy storage system include: ； In the formula, This represents the cycle life at a depth of discharge of 1. This represents the total equivalent cycle life within the scheduling period; The active power loss rate index includes: ； In the formula, flywheel energy storage unit m exist t Energy loss at any moment; flywheel energy storage unit m exist t The charging and discharging power at any given moment; The SOC balance index of the energy storage system includes: 。

Images