Hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling distribution

Abstract

The invention discloses a hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling distribution, and the method comprises the steps: monitoring the power grid frequency change rate of a target hybrid energy storage system in real time, and generating a total inertia power demand; based on the total inertia power demand, determining a super capacitor power distribution weight; obtaining an optimized power value of the super capacitor and an optimized power value of the energy storage battery according to the constructed objective function; based on the optimized power value of the super capacitor and the power value of the energy storage battery, correcting the power distribution weight of the super capacitor; and generating a super-capacitor inertia time constant and an energy storage battery inertia time constant based on the corrected super-capacitor power distribution weight. According to the method, the problem of frequency instability caused by insufficient inertia of a high-proportion new energy power grid is solved; the stability level of the power grid frequency is remarkably improved, the service life of the energy storage battery is greatly prolonged, and the whole life cycle operation and maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system stability control and new energy grid connection technology, more particularly to a hybrid energy storage inertia support optimization method based on real-time frequency rate dynamic decoupling distribution. BACKGROUND

[0002] With large-scale grid connection of intermittent power sources such as wind power and photovoltaic power, the decline of power system inertia leads to intensified frequency fluctuation. The traditional synchronous unit inertia response capacity is insufficient, and needs to rely on energy storage system to provide fast virtual inertia support. The existing hybrid energy storage inertia support strategy has the following defects:

[0003] (1) Static distribution limitation: fixed power distribution ratio cannot adapt to the transient mutation characteristics of power grid frequency, resulting in response lag or excessive dependence on batteries;

[0004] (2) Device characteristic mismatch: the millisecond response advantage of super capacitor and the sustained support capacity of battery are not decoupled and utilized as needed;

[0005] (3) Ignoring life and economy: frequent high-power charging and discharging accelerates battery aging and increases the life cycle cost.

[0006] Therefore, how to realize the precise matching of device characteristics and dynamic demand of power grid, and take into account the economy and equipment life, is a problem that needs to be solved by those skilled in the art. SUMMARY

[0007] In view of the above problems, the present application provides a hybrid energy storage inertia support optimization method based on real-time frequency rate dynamic decoupling distribution, to at least solve some of the technical problems mentioned in the background.

[0008] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0009] The present application provides a hybrid energy storage inertia support optimization method based on real-time frequency rate dynamic decoupling distribution, comprising the following steps:

[0010] Real-time monitoring of the power grid frequency rate of the target hybrid energy storage system, and generating a total inertia power demand;

[0011] Based on the total inertia power demand, determine the super capacitor power distribution weight;

[0012] According to the constructed objective function, obtain the optimized super capacitor power value and energy storage battery power value;

[0013] Based on the optimized super capacitor power value and energy storage battery power value, correct the super capacitor power distribution weight;

[0014] Generate the super capacitor inertia time constant and the energy storage battery inertia time constant based on the corrected super capacitor power distribution weight.

[0015] Further, determine the super capacitor power distribution weight based on the total inertia power demand; specifically including:

[0016] Generate the total inertia power demand change rate according to the real-time total inertia power demand;

[0017] Respectively normalize the total inertia power demand and the total inertia power demand change rate to generate the power demand scale and the power change urgency;

[0018] Determine the disturbance comprehensive intensity index according to the power demand scale and the power change urgency;

[0019] Calculate the super capacitor power distribution weight according to the disturbance comprehensive intensity index.

[0020] Further, the power demand scale is the ratio of the total inertia power demand to the system rated power; the power change urgency is the ratio of the total inertia power demand change rate to the preset power change rate reference value.

[0021] Further, the disturbance comprehensive intensity index is expressed as:

[0022] K sc_core =α·(1-e -βx )+γ·(1-e -δy )

[0023] Wherein, K sc_core represents the disturbance comprehensive intensity index; α represents the gain coefficient of the power demand scale x; β represents the sensitivity of the power demand scale x; γ represents the gain coefficient of the power change urgency y; δ represents the sensitivity of the power change urgency y.

[0024] Further, the super capacitor power distribution weight is expressed as:

[0025] K sc =λ·ω c ·(K sc_core ) η

[0026] ω c =ω min +(l1·x+l2·y)·(ω max -ω min )

[0027] Wherein, K sc represents the super capacitor power distribution weight; λ represents the guarantee K scGain coefficient in the interval [0, 1]; η represents a nonlinear adjustment factor; l1 represents a power scale normalization coefficient; l2 represents a power change rate normalization coefficient; ω c represents an adaptive cutoff frequency; ω min represents a lower limit of system frequency; ω max represents an upper limit of system frequency.

[0028] Further, the objective function is represented as:

[0029] F = min (α1·C sm1 + α2·C sm2 + β1·C c1 · P sc + β2·C c2 · P total ) s.t. P sc + P batt = P total

[0030] Wherein, F represents the objective function; α1 represents the weight coefficient of the super capacitor life loss coefficient C sm1 ; α2 represents the weight coefficient of the energy storage battery life loss coefficient C sm2 ; β1 represents the weight coefficient of the super capacitor degree electric operation cost C c1 ; β2 represents the weight coefficient of the energy storage battery degree electric operation cost C c2 ; P total represents the total inertia power demand; P sc represents the super capacitor response power value; P batt represents the energy storage battery response power value.

[0031] Further, the super capacitor power distribution weight is corrected based on the optimized super capacitor power value and energy storage battery power value; specifically including:

[0032] The ratio of the optimized super capacitor power value and energy storage battery power value is taken as an optimization factor;

[0033] The super capacitor power distribution weight is corrected based on the optimization factor.

[0034] Further, the corrected super capacitor power distribution weight is represented as:

[0035] K scf = λ scf · (K sc + K opt )

[0036] Wherein, K scf represents the corrected super capacitor power distribution weight, recorded as the final execution weight; λscf represents the final execution weight normalization coefficient; K sc represents the super capacitor power distribution weight; K opt represents the optimization factor.

[0037] Further, the super capacitor inertia time constant is represented as:

[0038]

[0039] wherein T Jsc represents the super capacitor inertia time constant; P total represents the total inertia power demand; f N represents the grid rated frequency; K scf represents the corrected super capacitor power distribution weight; P N is the system rated power; and RoCoF represents the grid frequency rate of change.

[0040] Further, the energy storage battery inertia time constant is represented as:

[0041]

[0042] wherein T Jbatt represents the energy storage battery inertia time constant; P total represents the total inertia power demand; f N represents the grid rated frequency; K scf represents the corrected super capacitor power distribution weight; P N is the system rated power; and RoCoF represents the grid frequency rate of change.

[0043] According to the technical solution described above, compared with the prior art, the application provides a hybrid energy storage inertia support optimization method based on real-time frequency rate of change dynamic decoupling distribution, which has the following beneficial effects:

[0044] The application adjusts the power distribution weight according to the grid frequency rate of change (RoCoF) gradient in real time, is used for coordinating the hybrid energy storage system of the super capacitor and the energy storage battery, and then performs system inertia support, solves the frequency instability problem caused by insufficient inertia of the high-proportion new energy power grid, significantly improves the stability level of the grid frequency, greatly prolongs the service life of the energy storage battery and reduces the whole life cycle operation and maintenance cost, plays a role of performance improvement and cost reduction, and increases economic benefits.

[0045] The technical solution of the application will be further described in detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 A schematic diagram of the process for optimizing hybrid energy storage inertia support based on dynamic decoupling allocation of real-time frequency change rate provided in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of a target hybrid energy storage system provided in an embodiment of the present invention. Detailed Implementation

[0049] 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.

[0050] This invention discloses a hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation, such as... Figure 1 As shown, it includes the following steps:

[0051] S1. Monitor the grid frequency change rate of the target hybrid energy storage system in real time and generate the total inertial power demand;

[0052] S2. Determine the power allocation weight of the supercapacitor based on the total inertia power requirement;

[0053] S3. Obtain the optimized supercapacitor power value and energy storage battery power value based on the constructed objective function;

[0054] S4. Based on the optimized power values ​​of the supercapacitor and the energy storage battery, the power allocation weight of the supercapacitor is corrected.

[0055] S5. Based on the corrected supercapacitor power allocation weights, generate the supercapacitor inertial time constant and the energy storage battery inertial time constant.

[0056] It should be noted that the above labels S1-S5 are only for the convenience of subsequent explanation and do not limit the execution order of each step; next, each of the above steps will be explained in detail.

[0057] In step S1 above, based on as Figure 2The target hybrid energy storage system shown monitors the grid frequency change rate (RoCoF) in real time and generates the total inertial power demand based on the grid frequency change rate:

[0058]

[0059] In the formula, RoCoF represents the rate of change of the power grid frequency; T J f represents the rated inertia time constant; N Indicates the rated frequency of the power grid; P N Indicates the rated power of the multilevel battery energy storage system;

[0060] In step S2 above, the power allocation weight of the supercapacitor is determined based on the total inertia power requirement; specifically, this includes:

[0061] S21. Generate the rate of change of total inertia power demand based on the real-time total inertia power demand;

[0062] S22. Normalize the total inertia power demand and the rate of change of total inertia power demand respectively, and map them to the interval [0,1] to generate the power demand scale and the urgency of power change; this step can eliminate the influence of dimensions and facilitate parameter tuning.

[0063] Wherein: the power demand scale is the ratio of the total inertia power demand to the system rated power; the power change urgency is the ratio of the total inertia power demand change rate to the preset power change rate benchmark value;

[0064]

[0065] Where x represents the power demand; P total P represents the total inertia power requirement; N Represents the system's rated power; y represents the urgency of power changes; R dP This represents the baseline value for the rate of change of power; This represents the rate of change in total inertia power demand.

[0066] S23. Determine the comprehensive disturbance intensity index based on the power demand scale and the urgency of power changes; expressed as:

[0067] K sc_core =α·(1-e -βx )+γ·(1-e -δy )

[0068] Where: K sc_core α represents the overall intensity index of disturbance; β represents the gain coefficient of power demand scale x; γ represents the sensitivity of power demand scale x; γ represents the gain coefficient of power change urgency y; and δ represents the sensitivity of power change urgency y.

[0069] S24. Calculate the supercapacitor power allocation weight based on the comprehensive disturbance intensity index; specifically, nonlinearly amplify and limit the output of the core calculated value to ensure that the weight is ultimately within the [0,1] interval and has extremely high sensitivity to high emergency situations; the supercapacitor power allocation weight is expressed as:

[0070] K sc =λ·ω c ·(K sc_core ) η

[0071] ω c =ω min +(l1·x+l2·y)·(ω max -ω min )

[0072] Among them, K sc λ represents the power allocation weight of the supercapacitor; λ represents the guarantee K sc The gain coefficient in the interval [0, 1]; η represents the nonlinear adjustment factor, which acts on the weight core value K in a power-law manner. sc_core ω is used to globally adjust the nonlinear characteristics of the decision function, and its value directly affects the system's response tendency under moderate and above emergency conditions; l1 represents the power scale normalization coefficient; l2 represents the power change rate normalization coefficient; ω min Indicates the lower limit of the system frequency; ω max Indicates the upper limit of the system frequency; ω c Indicates the adaptive cutoff frequency, ω c The boundary between high-frequency and low-frequency components in the total power command at the current moment is defined, and it is higher than the adaptive cutoff frequency ω. c Power fluctuations, identified as high-frequency components requiring rapid response, are allocated to the supercapacitor; those below the adaptive cutoff frequency ω c The power fluctuations are identified as "low-frequency components" that require continuous support and are allocated to the battery; specifically:

[0073] When the system is at rest (x→0, y→0): (l1·x+l2·y)→0, ω c →ω min The filter only allows extremely high frequency components to pass through the supercapacitor.

[0074] When the system experiences severe disturbances (x→1, y→1): (l1·x+l2·y)→1, ω c →ω max The filter also assigns lower frequency components to the supercapacitor for processing.

[0075] In step S3 above, a multi-objective optimization decision-making closed loop is performed. A battery life loss model and a levelized cost-per-kilowatt-hour model are embedded into the power allocation algorithm to construct an objective function, achieving a balance between support effectiveness, equipment lifespan, and economic efficiency. Then, based on the constructed objective function, the optimized supercapacitor power value and energy storage battery power value are obtained. The objective function is expressed as:

[0076] F=min(α1·C sm1 +α2·C sm2 +β1·C c1 ·P sc +β2·C c2 ·P total )stP sc +P batt =P total

[0077] Where F represents the objective function; α1 represents the supercapacitor lifetime loss coefficient C. sm1 The weighting coefficient; α2 represents the energy storage battery life loss coefficient C. sm2 The weighting coefficient; β1 represents the operating cost C of the supercapacitor per kilowatt-hour. c1 The weighting coefficient; β2 represents the levelized operating cost C of the energy storage battery. c2 Weighting coefficients; P total P represents the total inertia power requirement; sc This represents the power response of the supercapacitor, generated by the supercapacitor via a DC / AC converter, covering the 10–100 Hz frequency band; P batt This represents the response power value of the energy storage battery, which is generated by the energy storage battery through a DC / AC converter and covers the 0–10Hz frequency band.

[0078] This objective function reduces the frequency of high-gradient power commands to the battery by embedding a battery life loss model and a levelized cost per kilowatt-hour model, thereby reducing battery life loss and significantly reducing the total life cycle maintenance cost.

[0079] In step S4 above, the power allocation weight of the supercapacitor is corrected based on the optimized power values ​​of the supercapacitor and the energy storage battery; specifically, this includes:

[0080] The ratio of the optimized supercapacitor power value to the energy storage battery power value is used as the optimization factor:

[0081]

[0082] Among them, K opt P represents the optimization factor; scopt This represents the optimized supercapacitor power value; P battopt This represents the optimized power value of the energy storage battery;

[0083] Based on the optimization factor, the power allocation weight of the supercapacitor is corrected; the corrected power allocation weight of the supercapacitor is expressed as follows:

[0084] K scf =λ scf ·(K sc +K opt )

[0085] Among them, K scf This represents the corrected supercapacitor power allocation weight, denoted as the final execution weight; λ scf K represents the final weight normalization coefficient. sc This indicates the power allocation weight of the supercapacitor.

[0086] In step S5 above, the supercapacitor inertial time constant and the energy storage battery inertial time constant are generated based on the corrected supercapacitor power allocation weights:

[0087]

[0088] Among them, T Jsc T represents the inertial time constant of a supercapacitor; Jbatt P represents the inertial time constant of the energy storage battery. total Indicates the total inertia power requirement; f N Indicates the rated frequency of the power grid; K scf This represents the corrected power allocation weight for supercapacitors; P N RoCoF represents the system's rated power; RoCoF represents the rate of change of the grid frequency.

[0089] In summary, the hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation provided by this invention adjusts the power allocation weight in real time according to the grid frequency change rate (RoCoF) gradient. This is used to coordinate the hybrid energy storage system of supercapacitors and energy storage batteries, thereby providing system inertia support and solving the frequency instability problem caused by insufficient inertia in high-proportion renewable energy grids. It is applicable to renewable energy power plants, energy storage power plants, and other scenarios, and has strong compatibility. Based on the method proposed in this application, it helps to prioritize the use of supercapacitors to achieve millisecond-level rapid support when the frequency changes drastically (e.g., supercapacitor allocation weight > 90%); and to make better use of the energy capacity of energy storage batteries when the power demand is large or changes slowly.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation, characterized in that, Includes the following steps: Real-time monitoring of the grid frequency change rate of the target hybrid energy storage system and generation of total inertial power demand; Based on the total inertia power requirement, the power allocation weight of the supercapacitor is determined; The optimized power values ​​of the supercapacitor and the energy storage battery are obtained based on the constructed objective function. Based on the optimized supercapacitor power value and energy storage battery power value, the supercapacitor power allocation weight is corrected; Based on the corrected power allocation weights of the supercapacitor, the inertial time constants of the supercapacitor and the inertial time constants of the energy storage battery are generated.

2. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The determination of supercapacitor power allocation weights based on the total inertia power requirement specifically includes: Generate the rate of change of total inertia power demand based on the real-time total inertia power demand; The total inertia power demand and the rate of change of the total inertia power demand are normalized respectively to generate the power demand scale and the urgency of power change; The comprehensive disturbance intensity index is determined based on the power demand scale and the urgency of power changes. The power allocation weight of the supercapacitor is calculated based on the comprehensive disturbance intensity index.

3. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 2, characterized in that, The power demand scale is the ratio of the total inertia power demand to the system rated power; the power change urgency is the ratio of the total inertia power demand change rate to a preset power change rate benchmark value.

4. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 2, characterized in that, The comprehensive disturbance intensity index is expressed as follows: K sc_core =α·(1-e -βx )+γ·(1-e -δy ) Among them, K sc_core α represents the overall intensity index of disturbance; β represents the gain coefficient of power demand scale x; γ represents the sensitivity of power demand scale x; γ represents the gain coefficient of power change urgency y; and δ represents the sensitivity of power change urgency y.

5. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 2, characterized in that, The supercapacitor power allocation weight is expressed as follows: K sc =l·oh c ·(K sc_core ) η oh c =ω min +(l1 x+l2 y) (ω max -oh min ) Among them, K sc The supercapacitor power allocation weight is represented by λ; λ represents the guarantee K. sc Gain coefficient in the interval [0, 1]; η represents the nonlinear adjustment factor; l1 represents the power scale normalization coefficient; l2 represents the power change rate normalization coefficient; ω c Indicates the adaptive cutoff frequency; ω min Indicates the lower limit of the system frequency; ω max This indicates the upper limit of the system frequency.

6. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The objective function is expressed as: F=min(α1·C sm1 +α2·C sm2 +β1·C c1 ·P sc +β2·C c2 ·P total )s.t.P sc +P batt =P total Where F represents the objective function; α1 represents the supercapacitor lifetime loss coefficient C. sm1 The weighting coefficient; α2 represents the energy storage battery life loss coefficient C. sm2 The weighting coefficient; β1 represents the operating cost C of the supercapacitor per kilowatt-hour. c1 The weighting coefficient; β2 represents the levelized operating cost C of the energy storage battery. c2 Weighting coefficients; P total P represents the total inertia power requirement; sc P represents the power response value of the supercapacitor; batt This indicates the response power value of the energy storage battery.

7. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The process involves correcting the supercapacitor power allocation weight based on the optimized supercapacitor power value and energy storage battery power value; specifically, this includes: The ratio of the optimized supercapacitor power value to the energy storage battery power value is used as the optimization factor; Based on the optimization factor, the power allocation weight of the supercapacitor is corrected.

8. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The corrected supercapacitor power allocation weight is expressed as follows: K scf =λ scf ·(K sc +K opt ) Among them, K scf This represents the corrected supercapacitor power allocation weight, denoted as the final execution weight; λ scf K represents the final weight normalization coefficient. sc K represents the power allocation weight of the supercapacitor; opt This represents the optimization factor.

9. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The inertial time constant of the supercapacitor is expressed as: Among them, T Jsc P represents the inertial time constant of the supercapacitor; total Indicates the total inertia power requirement; f N Indicates the rated frequency of the power grid; K scf This represents the corrected power allocation weight for supercapacitors; P N RoCoF represents the system's rated power; RoCoF represents the rate of change of the grid frequency.

10. The hybrid energy storage inertia support optimization method based on real-time frequency change rate dynamic decoupling allocation according to claim 1, characterized in that, The inertial time constant of the energy storage battery is expressed as: Among them, T Jbatt P represents the inertial time constant of the energy storage battery. total Indicates the total inertia power requirement; f N Indicates the rated frequency of the power grid; K scf This represents the corrected power allocation weight for supercapacitors; P N RoCoF represents the system's rated power; RoCoF represents the rate of change of the grid frequency.

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