Method for evaluating operation reliability of wind storage combined system considering frequency stability constraint

Abstract

The application discloses a kind of wind storage combined system operation reliability evaluation methods considering frequency stability constraint, belong to power system safety and stability analysis and operation control technical field, including: the DFIG wind turbine-energy storage collaborative dynamic response refinement model considering frequency safety constraint is constructed;Based on frequency trajectory safety domain, the inertia demand analysis of system is carried out, and wind storage collaborative inertia layered optimization distribution strategy is established;The power system operation reliability quantitative evaluation model considering frequency cause risk is constructed;The integrated process from dynamic safety check to operation reliability evaluation is executed.The application solves the frequency instability risk caused by inertia reduction in high wind power penetration rate power system, and the problem that traditional reliability evaluation does not fully consider frequency dynamic influence.

Description

Technical Field

[0001] This invention belongs to the field of power system safety and stability analysis and operation control technology, specifically relating to a method for evaluating the operational reliability of a wind-storage integrated system considering frequency stability constraints. Background Technology

[0002] Currently, the penetration rate of renewable energy, represented by wind power, in the power system continues to increase. Wind turbines are connected to the grid via power electronic converters, and their operating mechanism differs fundamentally from that of traditional synchronous generators. This leads to a significant decrease in the system's equivalent inertia, weakening the grid's dynamic frequency support capability against power disturbances. When a large power deficit fault occurs in the system, it can easily cause a sharp drop in grid frequency, threatening the stable operation of synchronous generators and even triggering cascading failures, severely damaging the system's operational reliability. Existing technologies have explored the frequency support potential of wind power, strategies for energy storage to participate in frequency regulation, and system frequency stability assessment. However, the following shortcomings still exist:

[0003] 1. Most studies treat wind turbine frequency regulation, energy storage response and system frequency reliability assessment as independent components, lacking a unified quantitative modeling framework from dynamic constraints to steady-state reliability;

[0004] 2. Frequency safety boundaries are often treated as optimization targets or soft constraints, rather than being embedded as hard safety boundaries into the control strategy generation and reliability assessment process;

[0005] 3. Traditional reliability assessments mainly focus on static power supply insufficiency expectations, failing to quantify the indirect energy loss and increased generator tripping risk caused by frequency exceeding limits during frequency dynamic processes.

[0006] Therefore, there is an urgent need for an integrated method that can tightly couple frequency safety constraints, wind-storage coordinated control, and quantitative assessment of operational reliability. Summary of the Invention

[0007] To address the aforementioned shortcomings in existing technologies, this invention provides a method for evaluating the operational reliability of a wind-storage integrated system that considers frequency stability constraints. This method solves the problem of frequency instability risk caused by reduced inertia in power systems with high wind power penetration, as well as the issue that traditional reliability assessments do not fully consider the dynamic effects of frequency.

[0008] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: a method for evaluating the operational reliability of a wind-storage integrated system considering frequency stability constraints, comprising the following steps:

[0009] S1: Construct a refined dynamic response model of DFIG wind turbine-energy storage collaboration that takes into account frequency safety constraints;

[0010] The model includes an active frequency response sub-model of the DFIG wind turbine based on optimized power point tracking and an equivalent inertia-droop joint response sub-model of the generalized energy storage system.

[0011] S2: Conduct system inertia requirement analysis based on the frequency trajectory safety domain, and establish a wind-storage collaborative inertia hierarchical optimization allocation strategy, including:

[0012] S21: Establish the dynamic equation of the system at the disturbed frequency and solve the analytical solution of the frequency deviation in the time domain;

[0013] S22: Based on the preset minimum frequency threshold and steady-state frequency deviation limit, the minimum total equivalent inertia that the wind storage system needs to provide is solved in reverse.

[0014] S23: Construct an optimal allocation model with the goal of minimizing frequency regulation costs and the constraint of frequency security, and solve for the optimal allocation scheme of virtual inertia of each wind turbine and equivalent inertia of each energy storage unit;

[0015] S3: Construct a quantitative assessment model for the operational reliability of power systems that takes into account frequency-induced risk, including:

[0016] S31: Establish a frequency sensitivity conditional tripping probability model for synchronous generators;

[0017] S32: Transform the time-varying tripping probability in a single frequency event into an equivalent correction to the long-term average failure rate;

[0018] S33: Construct an indirect power shortage expectation index to quantify the additional power loss during frequency recovery in the transient process;

[0019] S4: Executes an integrated process from dynamic security checks to operational reliability assessments, including:

[0020] S41: Generate a set of anticipated failure scenarios;

[0021] S42: Perform frequency dynamic safety verification and reliability consequence assessment sequentially for each fault state;

[0022] S43: Calculate the expected system-level power shortage and the expected indirect power shortage through full-state probabilistic aggregation, and output the evaluation results.

[0023] Furthermore, the construction of the active frequency response sub-model of the DFIG wind turbine based on optimized power point tracking includes:

[0024] Establish optimized speed adjustment in power point tracking mode Deviation from system frequency Proportional relationship:

[0025]

[0026] in, The scaling factor is adjustable.

[0027] Based on the corrected rotor speed reference value Determine the equivalent curve coefficients :

[0028]

[0029]

[0030] in, This is the initial rotor speed reference value. The optimal coefficient;

[0031] Determine the increase in active power of the wind turbine :

[0032]

[0033] in, This refers to the active power output of the DFIG wind turbine. This is the original MPPT power.

[0034] Furthermore, the construction of the equivalent inertia-droop joint response sub-model of the generalized energy storage system includes:

[0035] Define the response frequency deviation of the energy storage system The dynamically adjusted power component It is proportional to the rate of change of the system frequency:

[0036]

[0037] in, For equivalent composite inertia, For time;

[0038] Determine the dynamic feasible region constraints for the energy storage output power:

[0039]

[0040]

[0041] in, For energy storage output power, , These are the upper and lower limits of net power output. , These are the upper and lower limits of dynamic power. This represents the upper limit of the rate of change of frequency; the superscript "." indicates the first derivative.

[0042] Furthermore, the dynamic equation for the disturbed frequency of the system is:

[0043]

[0044] in, For system frequency deviation, This is due to a power deficit. The system damping coefficient is... This represents the total power of the primary frequency modulation.

[0045] The time-domain analytical solution for the frequency deviation is:

[0046]

[0047] in, The moment of lowest frequency, This is the system's overall inertial constant.

[0048] Furthermore, the overall inertial constant of the system is:

[0049]

[0050] in, For the inertia of the synchronous machine, The virtual inertia provided for DFIG For the first The contribution of the equivalent inertia of a typhoon turbine. For the first The equivalent inertia contribution of the energy storage system. For the number of wind turbines, For the amount of energy stored;

[0051] Minimum total equivalent inertia We obtain the following by solving the equation:

[0052]

[0053]

[0054] in, The system's equivalent frequency function, The total equivalent inertia of the wind-storage system. The preset minimum frequency threshold, This is the point of lowest frequency.

[0055] Furthermore, the objective function of the optimized allocation model is:

[0056]

[0057] in, For the total frequency modulation cost, , These are the inertia cost equivalence coefficients for wind turbines and synchronous generator units, respectively. This is the lower limit of the total equivalent inertia of the wind-storage system. , The first Typhoon machine and the The upper limit of the equivalent inertia contribution of the energy storage group.

[0058] Furthermore, the frequency sensitivity conditional tripping probability model for the synchronous generator is as follows:

[0059]

[0060] in, This is a conditional tripping probability model. For real-time frequency, Based on the failure rate, , These are the absolute upper and lower limits for frequency protection operation. , These represent the upper and lower limits of the generator's normal operating frequency range.

[0061] Furthermore, the conversion of the time-varying tripping probability in a single frequency event into an equivalent correction to the long-term average failure rate specifically includes:

[0062] Calculate disturbance events The equivalent instantaneous failure rate of the generator during the frequency dynamic process is as follows:

[0063]

[0064] in, The equivalent instantaneous failure rate of the generator. The average failure rate of the generator. This is the proportionality coefficient. For a disturbance event The resulting frequency dynamic process;

[0065] Calculate the contribution of this event to the long-term mean failure rate. :

[0066]

[0067] in, For a disturbance event The duration of the triggered frequency dynamic process;

[0068] Calculate the annual equivalent mean failure rate of the generator after taking all frequency events into account. :

[0069]

[0070] in, The frequency of the event This is the set of all perturbation events that could potentially cause significant frequency exceedances.

[0071] Corrected generator long-term mean unavailability for:

[0072]

[0073] in, This represents the average repair rate of the generator.

[0074] Furthermore, the method for constructing the Indirect Energy Insufficiency Expectation Index (EIENS) includes:

[0075] Calculate fault status The next frequency modulation indirect loss :

[0076]

[0077] in, This marks the end of a single frequency modulation phase.

[0078] Calculate the indirect loss of second frequency modulation :

[0079]

[0080] in, This marks the end of the second frequency modulation phase.

[0081] Calculate fault status Total indirect losses :

[0082]

[0083] Computational system-level indirect power shortage expectation :

[0084]

[0085] in, System fault status The probability of occurrence, This represents the number of fault states.

[0086] Furthermore, S4 specifically includes:

[0087] The set of anticipated failure scenarios specifically includes:

[0088] Input the reference system parameters and set the quantization boundary for safe frequency operation, and generate a set of anticipated fault scenarios based on deterministic criteria or probabilistic sampling methods;

[0089] The process of sequentially performing frequency dynamic security checks and reliability consequence assessments for each fault state specifically includes:

[0090] For each fault in the set of anticipated fault scenarios, the dynamic power support of the wind-storage combined system is simulated based on the power deficit model established in step S1. The frequency dynamic equation in step S2 is substituted to solve the frequency time-domain response curve, and the lowest frequency point and steady-state value are extracted and a safety check is performed.

[0091] Substitute the frequency time-domain response curve into the model in step S3 to calculate the instantaneous tripping probability of each synchronous generator and correct its equivalent failure rate. Perform optimal load reduction calculation to obtain the direct load shedding amount, and at the same time calculate the indirect energy loss.

[0092] The method of calculating the expected system-level power shortage and the expected indirect power shortage through full-state probability aggregation, and outputting the evaluation results, specifically includes:

[0093] Calculate the probability of occurrence of each fault state, and aggregate the expected power shortage at the system level and the expected power shortage in the indirect system.

[0094] The beneficial effects of this invention are:

[0095] The method proposed in this invention uses dynamic safety constraints such as the lowest frequency point and steady-state deviation as hard conditions to inversely solve and optimize the coordinated frequency regulation strategy of the wind-storage system. This achieves an inherent unity between transient safety analysis and operational reliability assessment, making the assessment results more aligned with the urgent frequency stability requirements of a high-proportion renewable energy grid. By introducing optimized power point tracking control (DFIG) and the equivalent inertial response model of energy storage, a coordinated mechanism of "economic priority and rapid replenishment" is constructed. This fully taps the frequency regulation potential of wind turbines while ensuring the speed and economy of frequency support.

[0096] This invention innovatively proposes the "indirect power shortage expectation" index and establishes a generator failure probability model that takes into account frequency-induced risk. This enables a more comprehensive and accurate quantification of the impact of frequency dynamics on the long-term power supply reliability of the system. Ultimately, the entire methodology is clear in its model and process, possesses good engineering applicability, and can provide a quantitative decision-making basis for dispatching operations that combines safety and economy, effectively improving the grid's adaptability to high-proportion renewable energy integration. Attached Figure Description

[0097] Figure 1 The flowchart shows the wind-storage integrated system operation reliability assessment method considering frequency stability constraints of this invention.

[0098] Figure 2 This is a schematic diagram comparing the power-speed curves of a DFIG fan under MPPT and OPPT control modes.

[0099] Figure 3 This is a schematic diagram of the power constraint of the generalized energy storage inertial response model in an embodiment of the present invention.

[0100] Figure 4 This is a schematic diagram of the system's frequency dynamic response and primary frequency modulation power change after being subjected to power disturbance.

[0101] Figure 5 A schematic diagram showing the relationship between generator tripping probability and system frequency, taking into account the influence of frequency.

[0102] Figure 6 This is a comparison chart of the system frequency dynamic response curves when frequency constraints and wind-storage coordination are introduced / not introduced in the embodiments.

[0103] Figure 7 This is a schematic diagram illustrating the impact of system frequency reduction on the annual failure rate of the synchronous generator set in the embodiment. Detailed Implementation

[0104] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0105] like Figure 1 As shown, a method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints includes the following steps:

[0106] S1: Construct a refined dynamic response model of DFIG wind turbine-energy storage collaboration that takes into account frequency safety constraints;

[0107] The core of this step is to establish a quantifiable and controllable mathematical model of the frequency response of wind turbines and energy storage, so as to provide accurate action basis for their participation in system frequency regulation.

[0108] The model includes an active frequency response sub-model of the DFIG wind turbine based on optimized power point tracking and an equivalent inertia-droop joint response sub-model of the generalized energy storage system.

[0109] The construction of the active frequency response sub-model for the DFIG wind turbine based on optimized power point tracking includes:

[0110] In MPPT mode, the operating objective of the DFIG is to capture maximum wind energy, and its active power output PMPPT is the generator speed. A piecewise function is used to ensure optimal wind energy capture at rated wind speed. This relationship forms the benchmark for subsequent control strategies and can be uniformly expressed as:

[0111] (1)

[0112] Among them, the function The specific form is determined by the aerodynamic characteristics and control system of the fan.

[0113] When the system frequency deviates At this time, the OPPT controller intervenes. The control law is designed for speed adjustment. Frequency deviation Proportional:

[0114] (2)

[0115] in, It is an adjustable proportional coefficient used to set the strength of the frequency support;

[0116] Based on the corrected rotor speed reference value Determine the equivalent curve coefficients :

[0117] Corrected rotor speed reference value Initial reference value Sum of adjustment amounts:

[0118] (3)

[0119] like Figure 2 As shown, point A is the optimal operating point in MPPT mode. Within a specific wind speed range, the power-speed relationship of the fan can usually be determined by an optimal coefficient. To approximate the description, such that P∝ When a power disturbance occurs, the fan power increases from point A to point B, while the reference speed value changes from... Adjusted to The operating point gradually shifts from B to C, and then during the speed recovery phase, it recovers from point C to point A. This is equivalent to correcting the proportional coefficient of the power-speed curve at the current operating point. This is to ensure that the adjusted speed... Even under these conditions, the wind turbine can still track a path that passes through an area shaped like P∝k. The optimal power curve after translation, and the new equivalent curve coefficients. The following relationship must be satisfied:

[0120] (4)

[0121] Will Substituting the expression into the above equation, we can obtain the equivalent curve coefficients under OPPT control as follows:

[0122] (5)

[0123] in, This is the initial rotor speed reference value. The optimal coefficient;

[0124] Based on the corrected coefficients When frequency deviation occurs At that time, the active power output of DFIG Compared to the original MPPT power Change This refers to the power supported by the provided frequency. At a rotational speed of... Under the operating conditions, this increment can be expressed as:

[0125] (6)

[0126] Will Substituting the expression, the increase in the active power of the wind turbine is clearly shown. It is the system frequency deviation The function. A negative frequency deviation will produce a positive [value]. This injects active power into the system, thereby achieving virtual inertia support; and vice versa.

[0127] Through the above formulaic derivation, the dynamic process of DFIG participating in system frequency regulation based on OPPT control is fully characterized: system frequency deviation The control law maps the speed adjustment amount. This leads to the correction of the power curve coefficient. Ultimately, this manifests as a quantifiable increase in active power. This theoretical framework provides a framework for coordinating control parameters. It provides an accurate mathematical model for tuning and performance optimization.

[0128] The equivalent inertia-droop joint response model of a generalized energy storage system is rigorously explained and formalized in the following section. The core of this model lies in the introduction of an equivalent composite inertia. This key parameter quantifies and characterizes the rapid and controllable power response characteristics of the energy storage system as an inertial support capability similar to that of a synchronous generator, thus providing an accurate mathematical model for its participation in the primary frequency regulation of the system.

[0129] The construction of the equivalent inertia-droop joint response sub-model of the generalized energy storage system includes:

[0130] like Figure 3 As shown, the output power of the energy storage system when participating in frequency regulation is... It is not only used to balance power deficits, but also needs to provide an inertial response to frequency changes. This dynamic behavior can be described by the following equation:

[0131] (7)

[0132] in, To contribute to the initial plan, In response to frequency deviation The dynamically adjusted power component is defined to simulate the inertial response characteristics of a synchronous machine. It is proportional to the rate of change of the system frequency:

[0133] (8)

[0134] in, Equivalent composite inertia, its physical meaning is to measure the equivalent power support strength that an energy storage system can provide to suppress a unit rate of frequency change, and its unit is the same as the synchronous machine inertial constant. A negative sign indicates that the power output direction is opposite to the frequency change trend;

[0135] The actual power output of energy storage must simultaneously meet its inherent charge and discharge capacity limitations. Considering frequency support requirements, its dynamic power upper and lower limits are... and In addition to the static limits, a power capacity that must be reserved to provide inertial response needs to be added. Therefore, the energy storage output must meet the following requirements at all times:

[0136] (9)

[0137] This formula constitutes the dynamic feasible domain constraint for energy storage to participate in frequency regulation, and clearly reveals the coupling relationship among output power, equivalent inertia and real-time frequency change rate.

[0138] Actual power system operating procedures typically set a safe upper limit for the rate of frequency change, i.e. To ensure that the energy storage output does not exceed the limit under the worst-case frequency variation scenario, the dynamic constraints can be conservatively treated, and its corresponding static feasible region can be derived. Substituting the extreme value of the rate of frequency change into the constraint equation, we get:

[0139] (10)

[0140] To more clearly characterize this, we define the upper and lower limits of net power output, taking into account the worst-case rate of change.

[0141] (11)

[0142] The static constraint can then be concisely rewritten as:

[0143] (12)

[0144] in, For energy storage output power, , These are the upper and lower limits of net power output. , These are the upper and lower limits of dynamic power. This represents the upper limit of the rate of change of frequency; the superscript "." indicates the first derivative.

[0145] This formula shows that the equivalent inertia The value of directly determines the feasible range that can be used for steady-state power regulation. , ]. The larger the value, the greater the power capacity reserved to cope with frequency changes, and the stronger its inertial response capability, but the power margin available for other adjustment purposes is correspondingly reduced.

[0146] Based on the above derivation, the equivalent composite inertia This becomes a core controllable parameter that connects frequency safety requirements with energy storage operation boundaries. The dispatch or control system can adjust this parameter online. The numerical value allows for flexible and precise configuration of the virtual inertia intensity of energy storage during primary frequency regulation: increasing... It can enhance the system's ability to suppress the rate of frequency change, making it suitable for high-risk scenarios with severe inertia shortage; it reduces... This allows for the release of more power regulation capacity for energy throughput or other ancillary services. This model provides a parameterized and quantitative description of the inertial support capacity of energy storage, laying a solid theoretical foundation for capacity allocation and coordinated optimization in wind-storage coordinated frequency control.

[0147] S2: Conduct system inertia requirement analysis based on the frequency trajectory safety domain, and establish a wind-storage collaborative inertia hierarchical optimization allocation strategy;

[0148] The core of this step lies in establishing a quantitative mapping relationship between the frequency safety boundary and the system's required inertia support. Through rigorous mathematical derivation, the operating criteria of the minimum frequency point and steady-state value are transformed into calculable constraints, and the minimum equivalent inertia that the wind-storage system must provide is solved in reverse, ultimately forming a collaborative allocation strategy based on optimization theory, specifically including:

[0149] S21: Establish the dynamic equation of the system at the disturbed frequency and solve the analytical solution of the frequency deviation in the time domain;

[0150] like Figure 4 As shown, when the system experiences an event of size... During a step-type power deficit disturbance, the frequency drops to its lowest point. Within a timescale, assuming that the conventional generating units and wind-storage systems participating in primary frequency regulation output power uniformly according to their droop coefficients, their total power... It can be approximated as linear growth, that is Taking into account the frequency regulation effect of the system load, the classical rotor motion equation describing the system frequency dynamics can be written as:

[0151] (13)

[0152] in, For system frequency deviation, This is due to a power deficit. The system damping coefficient is... This represents the total power of the primary frequency modulation.

[0153] Will Substituting the expression into the equation, we obtain the non-homogeneous linear differential equation:

[0154] (14)

[0155] Assuming the initial frequency deviation Δf(0) = 0, solve equation (14). Its homogeneous solution is... The particular solution obtained using the method of variation of constants is in the form of: Substituting the particular solution into equation (14), we get:

[0156] (15)

[0157] From initial conditions Determine constants Therefore, the time-domain analytical solution for the frequency deviation is:

[0158] (16)

[0159] Lowest frequency moment This can be achieved by setting the first derivative The equation is obtained, which satisfies:

[0160] (17)

[0161] This equation is a transcendental equation, and its solution characterizes the inertia. The impact on the frequency drop rate. Substituting into equation (16), the point of lowest frequency can be obtained. The analytical expression for steady-state frequency deviation. This can be obtained by letting t→∞:

[0162] (18)

[0163] This equation shows that the steady-state deviation is determined only by the power deficit and system damping, and is independent of the system inertia.

[0164] S22: Based on the preset minimum frequency threshold and steady-state frequency deviation limit, the minimum total equivalent inertia that the wind storage system needs to provide is solved in reverse.

[0165] Set a hard constraint for safe frequency operation: the lowest frequency point must not be lower than a threshold. (e.g., -0.5 Hz), steady-state deviation not exceeding (e.g., -0.2 Hz). The steady-state constraint can be directly transformed from equation (18) into a constraint on the power deficit. Or the requirement for total system damping D. The minimum point constraint needs to be... The expression is related to the threshold.

[0166] System composite inertia constant Due to the inertia of the synchronous machine Virtual inertia provided by DFIG and energy storage equivalent inertia Together they constitute:

[0167] (19)

[0168] in, For the inertia of the synchronous machine, The virtual inertia provided for DFIG For the first The contribution of the equivalent inertia of a typhoon turbine. For the first The equivalent inertia contribution of the energy storage system. For the number of wind turbines, For the amount of energy stored;

[0169] Substituting the specific power response models of the wind turbine and energy storage derived in equation (19) and S1 into the frequency dynamic equation (14) and solution (15), the lowest frequency point is found. This can be expressed as total power deficit. Synchronous machine inertia and the total equivalent inertia of the wind storage system Functions:

[0170] (20)

[0171] Minimum point safety constraints Therefore, it is transformed into about Inequalities:

[0172] (twenty one)

[0173] By solving inequality (21), the corresponding parameters for ensuring any anticipated fault can be calculated analytically or numerically. The minimum total equivalent inertia that the wind-storage system must provide is a frequency not lower than the safety threshold. .

[0174] S23: Construct an optimal allocation model with the goal of minimizing frequency regulation costs and the constraint of frequency security, and solve for the optimal allocation scheme of virtual inertia of each wind turbine and equivalent inertia of each energy storage unit;

[0175] To obtain the minimum total equivalent inertia required by the system Subsequently, optimal allocation needs to be performed between the wind turbine cluster and the energy storage system. The decision variable is defined as the virtual inertia of each wind turbine. and the equivalent inertia of each energy storage unit The allocation needs to comprehensively consider both economic efficiency and response speed, and the following optimization model can be constructed:

[0176] The objective function aims to minimize the total frequency regulation cost, where the frequency regulation cost of wind turbines is mainly related to load shedding losses and mechanical wear, while the energy storage cost is related to cycle life reduction.

[0177] (twenty two)

[0178] in, For the total frequency modulation cost, , These are the inertia cost equivalence coefficients for wind turbines and synchronous generator units, respectively. This is the lower limit of the total equivalent inertia of the wind-storage system. , The first Typhoon machine and the The upper limit of the equivalent inertia contribution of the energy storage group.

[0179] To prioritize economic efficiency and facilitate rapid replenishment, a frequency change rate threshold is introduced. As a switching condition. In the initial stage of frequency drop. Prioritize utilizing the wind turbine's inertia; when the drop accelerates, energy storage needs to instantly compensate for the difference. This logic can be approximated as a constraint on the inertia allocation ratio:

[0180] (twenty three)

[0181] in, For safety reasons, To estimate the maximum rate of change of frequency.

[0182] Solving this optimization model will yield the optimal inertia allocation scheme. , This scheme mathematically guarantees frequency security and achieves an optimal balance between economy and dynamic performance, thereby realizing refined coordination of the wind-storage system in the spatiotemporal dimensions.

[0183] S3: Construct a quantitative assessment model for the operational reliability of power systems that takes into account frequency-induced risk;

[0184] This step aims to construct a theoretical framework for quantitatively assessing operational reliability in the context of dynamic frequency processes. This framework establishes a mapping model between generator tripping risk and real-time system frequency, corrects traditional static reliability parameters, and innovatively proposes an index system for quantifying energy loss during frequency transient processes. This enables a synergistic measurement of the dual risks of static capacity insufficiency and dynamic frequency instability.

[0185] S31: Establish a frequency sensitivity conditional tripping probability model for synchronous generators;

[0186] like Figure 5 As shown, the protection system of a synchronous generator set will operate when the frequency exceeds the limit, and its tripping risk is the real-time frequency. The function. To quantify this risk, this invention establishes a piecewise linear conditional tripping probability model. .

[0187] Let the normal operating frequency range of the generator be [ , Within this range, the tripping probability remains at a relatively low baseline value. This corresponds to the inherent failure rate, which is not frequency-dependent. The absolute upper and lower limits of frequency protection operation are respectively... and When the frequency lies between the protection setpoint and the normal boundary, the probability of protection action changes linearly with the degree of frequency deviation. This model can be rigorously expressed by the following piecewise function:

[0188] (twenty four)

[0189] This model accurately characterizes the generator tripping probability from the base value during frequency transient processes. The dynamic process of nonlinearly climbing towards the inevitable action value of 1 provides a key quantitative tool for assessing the impact of frequency security on component reliability.

[0190] S32: Transform the time-varying tripping probability in a single frequency event into an equivalent correction to the long-term average failure rate;

[0191] In traditional reliability assessments, the average failure rate of a generator is... and average repair rate It is a constant, and its steady-state unavailability rate is... When considering frequency-related risks, the time-varying tripping probability in a single frequency event needs to be converted into an equivalent correction to the long-term average failure rate.

[0192] Consider a disturbance event The frequency dynamic process triggered Its duration is During this process, the generator's equivalent instantaneous failure rate It is the sum of its inherent failure rate and the frequency-induced risk rate. Assume the additional risk rate and the conditional trip probability... If they are directly proportional, then:

[0193] (25)

[0194] in, is a proportionality coefficient used to map probability to risk rate. The contribution of this event to the long-term average failure rate can be calculated by integrating its equivalent average failure rate:

[0195] (26)

[0196] Combined with the frequency of the event (Number of failures per year) can be calculated to account for all relevant frequency events, representing the generator's annual equivalent mean failure rate. :

[0197] (27)

[0198] In the formula, This is the set of all disturbance events that could potentially cause significant frequency out-of-limits occurrences. Ultimately, this is the corrected generator long-term average unavailability rate. for:

[0199] (28)

[0200] This modification transforms the generator's reliability parameters from static values ​​into variables that are dynamically related to the system's frequency safety level, and can be embedded into system-level reliability assessments using a Markov chain model.

[0201] S33: Construct an indirect power shortage expectation index to quantify the additional power loss during frequency recovery in the transient process;

[0202] To overcome the limitation that traditional Energy Insufficiency Expectation (EENS) can only assess static capacity insufficiency, this invention proposes an indirect Energy Insufficiency Expectation (EIENS) index to quantify the additional energy loss caused by the persistent frequency deviation during the transient process of frequency recovery.

[0203] Defined in system fault state The probability of its occurrence is as follows: The end of the first frequency modulation phase is At this point, the frequency has not fully recovered, and there is a residual power deficit. The second frequency modulation stage ends at... The indirect energy loss during the frequency recovery process can then be decomposed as follows:

[0204] Indirect losses from primary frequency modulation This stems from the power deficit that occurs between the end of the first frequency regulation and the start of the second frequency regulation.

[0205] (29)

[0206] Secondary frequency modulation indirect loss This stems from the fact that during the secondary frequency modulation process, the regulating power increases linearly but has not yet fully compensated for the losses during the deficit period.

[0207] (30)

[0208] In the formula, This represents the total primary frequency regulation power of the system. Therefore, the state... The total indirect losses are .

[0209] The overall reliability assessment metrics at the system level consist of the following two metrics:

[0210] Traditional power shortage expectation: measures load reduction resulting from the permanent withdrawal of generating capacity.

[0211] (31)

[0212] in, For state The load shedding amount caused by insufficient static capacity.

[0213] Indirect energy deficit expectation: measures the additional energy loss caused by the recovery delay of frequency dynamic processes.

[0214] (32)

[0215] Ultimately, the operational reliability of the system is jointly characterized by these two indicators. EENS characterizes the static adequacy risk of the system, while EIENS directly measures the dynamic frequency security risk of the system. The combination of the two enables a comprehensive and detailed assessment of the power system's "static load loss" and "load loss caused by dynamic frequency instability".

[0216] S4: Execute an integrated process from dynamic security verification to operational reliability assessment;

[0217] The integrated evaluation process proposed in this invention constructs a closed-loop analysis framework from transient safety verification to long-term risk quantification by organically integrating the aforementioned dynamic response model (S1), safety constraint optimization model (S2), and reliability quantification model (S3). This framework takes a set of anticipated failures as input and outputs quantitative indicators characterizing the overall operational risk of the system through state-by-state simulation and full probability aggregation. The specific execution steps are as follows:

[0218] S41: Generate a set of anticipated failure scenarios;

[0219] Input baseline system parameters, including network topology, conventional synchronous unit parameters, load level, and operating status and capacity configuration of DFIG wind turbine group and energy storage system;

[0220] Set quantization boundaries for safe frequency operation, such as a minimum frequency threshold. Steady-state frequency deviation limit and frequency change rate limit ;

[0221] Based on deterministic criteria or probabilistic sampling methods, a set of anticipated failure scenarios to be evaluated is generated. Each of the faults The disturbance type, location, and power deficit were defined. .

[0222] S42: Perform frequency dynamic safety verification and reliability consequence assessment sequentially for each fault state;

[0223] For fault set Each fault in Perform the following sub-steps:

[0224] Based on the power deficit after the fault The DFIG-OPPT model established in step S1 and the generalized energy storage equivalent inertial model are used to simulate the dynamic power support of the wind-storage combined system. ;

[0225] Substituting the wind-storage support power and the primary frequency regulation power of the conventional unit into the system frequency dynamic equation (16) derived in step S2, the system frequency time-domain response curve under this fault is obtained. ;

[0226] from Extracting key security indicators: Lowest frequency point and steady-state value Determine whether it meets the safety constraints set in step S1. Simultaneously, record the actual inertia support configuration and power output process required by the wind-storage system to ensure the frequency trajectory meets the constraints, as the control strategy for this fault state.

[0227] For faults that have completed security checks Based on its frequency response results And wind and storage control strategies, and assess their reliability consequences:

[0228] frequency curve Substitute into equation (24) to calculate the instantaneous tripping probability of each synchronous generator unit during the fault process, and then correct its equivalent fault rate through equations (25)-(28);

[0229] Based on the corrected equipment availability status, optimal load reduction calculations are performed to obtain the direct load shedding amount caused by the fault. At the same time, based on the time of the frequency recovery process , And power deficit, indirect energy loss is calculated using equations (29)-(30). .

[0230] S43: Calculate the expected system-level power shortage and the expected indirect power shortage through full-state probabilistic aggregation, and output the evaluation results.

[0231] After completing all After independent assessment of each fault state, system-level aggregation is performed:

[0232] Determine or calculate each fault state probability of occurrence ;

[0233] By aggregating direct and indirect load losses, the overall reliability index of the system is calculated: expected power shortage and expected indirect power shortage.

[0234] The final output is a system operational reliability assessment report, the core contents of which include the numerical values ​​of system-level reliability indicators, the ranking of key cause failures and their risk contribution analysis.

[0235] In one embodiment of the present invention, to verify the effectiveness of the proposed wind-storage coordinated control and reliability assessment method considering frequency stability constraints, a simulation example is constructed based on an improved IEEE-RTS79 test system. The improvement scheme involves shutting down corresponding synchronous generator units according to unit power generation cost based on different wind power penetration rates, thereby reducing the overall system operating cost. Two comparative cases are set up to comprehensively evaluate the performance of the proposed method (Case 3) from different dimensions: Case 1 aims to verify the core effectiveness of the proposed method in improving transient frequency security and reducing equipment risk; Case 2 further explores the adaptability and comprehensive reliability benefits of the method under different wind power penetration levels.

[0236] Example 1: Analysis of Frequency Dynamic Security and Equipment Risk Suppression Effects

[0237] This case study assumes a system wind power penetration rate of 30% and simulates a 197 MW synchronous generator unit experiencing a momentary grid disconnection fault at t=60 s, resulting in a power deficit of approximately 204 MW. Even with existing conventional units already at their maximum output capacity, the system still needs to address a net power deficit of approximately 283 MW. To highlight the advantages of the proposed coordinated control strategy, it is compared with two benchmark schemes: Case 1 wind power operates only in MPPT mode and lacks active frequency response capability; Case 2 wind power possesses active frequency regulation capability based on OPPT, but it is not coordinated with energy storage and no frequency safety constraints are applied.

[0238] Figure 6 The dynamic response trajectories of the system frequency under three case studies are presented. The results show that in Case 1, where the traditional turbine alone regulates the frequency, the lowest system frequency drops to -1.9902 Hz, posing a significant risk of instability. After introducing wind power frequency regulation (Case 2), the lowest frequency increases to -1.3625 Hz. However, when the wind-storage coordinated frequency constraint control strategy proposed in this invention is adopted (Case 3), the lowest frequency is significantly limited to -0.5 Hz, fully meeting the preset ±0.5 Hz safe operating boundary. This improvement stems from the rapid release of DFIG rotor kinetic energy by OPPT control and the millisecond-level power replenishment provided by energy storage after detecting an excessive frequency change rate; the two work together to effectively curb the initial rate and depth of frequency drop.

[0239] More in-depth equipment risk analysis, such as Figure 7 As shown, taking a 400 MW synchronous generator in the system as an example, its tripping probability is closely related to the operating frequency. In the severe frequency drop scenario of Case 1, the equivalent annual failure rate of this unit is as high as 33.03 times. Case 3, by maintaining a higher minimum frequency point, shifts the unit's operating point to a safer region of the tripping probability curve, thereby significantly reducing its annual failure rate to 20.45 times, a reduction of 38.1%. This quantitatively confirms that the proposed method, by ensuring the transient frequency safety of the system, can effectively reduce the risk of synchronous generator units being disconnected from the grid due to frequency protection actions, and enhance the system's anti-disturbance capability.

[0240] Example 2: System operational reliability assessment under different wind power penetration rates

[0241] To evaluate the universality and reliability improvement effect of the proposed method under different renewable energy access levels, this case study compares and analyzes the reliability indicators of the three schemes under two wind power penetration scenarios: 30% and 45%. Based on the full-state enumeration method (covering the N-1 fault set) and combined with the reliability model considering the frequency effect established above, the expected energy deficit (EENS) and expected indirect energy deficit (EIENS) of the system were calculated, and the results are summarized in Tables 1 and 2.

[0242] Table 1 Comparison of System Reliability Indicators at a Wind Power Penetration Rate of 30%

[0243] Case 1 37,815.73 126,664.68 Case 2 37,808.96 126,664.68 Case 3 39,107.60 126,563.33

[0244] Table 2 Comparison of System Reliability Indicators at a Wind Power Penetration Rate of 45%

[0245] Case 1 123,247.78 301,691.15 Case 2 123,238.63 301,691.15 Case 3 124,456.91 301,398.57

[0246] Analysis of the data in Table 1 shows that, at a penetration rate of 30%, the EENS for Case 3 is 126,563.33 MW·h, slightly lower than the previous two cases. This indicates that the proposed method reduces the risk of load shedding due to frequency collapse by maintaining frequency stability. However, its EENS is slightly higher than the benchmark case. This is because, during wind-storage coordinated frequency regulation, the rotational speed recovery characteristics of the DFIG slightly prolong the transition time of the first frequency regulation, resulting in slightly more indirect energy loss. This reveals the delicate trade-off between dynamic performance and steady-state energy loss in the optimal allocation of frequency regulation resources.

[0247] When wind power penetration reaches 45% (Table 2), the system inertia level further decreases, and the frequency stability problem becomes more prominent. The EENS in Case 3 is 301,398.57 MW·h, a more significant decrease compared to 301,691.15 MW·h in Cases 1 and 2. This indicates that in the challenging scenario of high wind power penetration and low inertia, the proposed frequency constraint collaborative control strategy becomes increasingly valuable in preventing large-scale load loss and ensuring power supply reliability. Although EENS still increases, considering the significant reduction in EENS, the overall reliability benefit of the proposed solution is positive. The comparison at the two penetration levels demonstrates that the method of this invention can effectively adapt to different renewable energy ratios, especially in high penetration scenarios, where its contribution to improving system operational reliability is more significant.

[0248] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the invention.

Claims

1. A method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints, characterized in that, Includes the following steps: S1: Construct a refined dynamic response model of DFIG wind turbine-energy storage collaboration that takes into account frequency safety constraints; The model includes an active frequency response sub-model of the DFIG wind turbine based on optimized power point tracking and an equivalent inertia-droop joint response sub-model of the generalized energy storage system. S2: Conduct system inertia requirement analysis based on the frequency trajectory safety domain, and establish a wind-storage collaborative inertia hierarchical optimization allocation strategy, including: S21: Establish the dynamic equation of the system at the disturbed frequency and solve the analytical solution of the frequency deviation in the time domain; S22: Based on the preset minimum frequency threshold and steady-state frequency deviation limit, the minimum total equivalent inertia that the wind storage system needs to provide is solved in reverse. S23: Construct an optimal allocation model with the goal of minimizing frequency regulation costs and the constraint of frequency security, and solve for the optimal allocation scheme of virtual inertia of each wind turbine and equivalent inertia of each energy storage unit; S3: Construct a quantitative assessment model for the operational reliability of power systems that takes into account frequency-induced risk, including: S31: Establish a frequency sensitivity conditional tripping probability model for synchronous generators; S32: Transform the time-varying tripping probability in a single frequency event into an equivalent correction to the long-term average failure rate; S33: Construct an indirect power shortage expectation index to quantify the additional power loss during frequency recovery in the transient process; S4: Executes an integrated process from dynamic security checks to operational reliability assessments, including: S41: Generate a set of anticipated failure scenarios; S42: Perform frequency dynamic safety verification and reliability consequence assessment sequentially for each fault state; S43: Calculate the expected system-level power shortage and the expected indirect power shortage through full-state probabilistic aggregation, and output the evaluation results.

2. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 1, characterized in that, The construction of the active frequency response sub-model for the DFIG wind turbine based on optimized power point tracking includes: Establish optimized speed adjustment in power point tracking mode Deviation from system frequency Proportional relationship: in, The scaling factor is adjustable. Based on the corrected rotor speed reference value Determine the equivalent curve coefficients : in, This is the initial rotor speed reference value. The optimal coefficient; Determine the increase in active power of the wind turbine : in, This refers to the active power output of the DFIG wind turbine. This is the original MPPT power.

3. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 2, characterized in that, The construction of the equivalent inertia-droop joint response sub-model of the generalized energy storage system includes: Define the response frequency deviation of the energy storage system The dynamically adjusted power component It is proportional to the rate of change of the system frequency: in, For equivalent composite inertia, For time; Determine the dynamic feasible region constraints for the energy storage output power: in, For energy storage output power, , These are the upper and lower limits of net power output. , These are the upper and lower limits of dynamic power. This represents the upper limit of the rate of change of frequency; the superscript "." indicates the first derivative.

4. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 3, characterized in that, The dynamic equation for the system's disturbed frequency is: in, For system frequency deviation, This is due to a power deficit. The system damping coefficient is... This represents the total power of the primary frequency modulation. The time-domain analytical solution for the frequency deviation is: in, The moment of lowest frequency, This is the system's overall inertial constant.

5. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 4, characterized in that, The overall inertial constant of the system is: in, For the inertia of the synchronous machine, The virtual inertia provided for DFIG For the first The contribution of the equivalent inertia of a typhoon turbine. For the first The equivalent inertia contribution of the energy storage system. For the number of wind turbines, For the amount of energy stored; Minimum total equivalent inertia We obtain the following by solving the equation: in, The system's equivalent frequency function, The total equivalent inertia of the wind-storage system. The preset minimum frequency threshold, This is the point of lowest frequency.

6. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 5, characterized in that, The objective function of the optimized allocation model is: in, For the total frequency modulation cost, , These are the inertia cost equivalence coefficients for wind turbines and synchronous generator units, respectively. This is the lower limit of the total equivalent inertia of the wind-storage system. , The first Typhoon machine and the The upper limit of the equivalent inertia contribution of the energy storage group.

7. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 6, characterized in that, The frequency sensitivity conditional tripping probability model for the synchronous generator is as follows: in, For conditional tripping probability models, For real-time frequency, Based on the failure rate, , These are the absolute upper and lower limits for frequency protection operation. , These represent the upper and lower limits of the generator's normal operating frequency range.

8. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 7, characterized in that, The process of converting the time-varying tripping probability in a single frequency event into an equivalent correction to the long-term average failure rate specifically includes: Calculate disturbance events The equivalent instantaneous failure rate of the generator during the frequency dynamic process is as follows: in, The equivalent instantaneous failure rate of the generator. The average failure rate of the generator. This is the proportionality coefficient. For a disturbance event The resulting frequency dynamic process; Calculate the contribution of this event to the long-term mean failure rate. : in, For a disturbance event The duration of the triggered frequency dynamic process; Calculate the annual equivalent mean failure rate of the generator after taking all frequency events into account. : in, The frequency of the event This is the set of all perturbation events that could potentially cause significant frequency exceedances. Corrected generator long-term mean unavailability for: in, This represents the average repair rate of the generator.

9. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 8, characterized in that, The method for constructing the Indirect Energy Shortage Expectation Index (EIENS) includes: Calculate fault status The next frequency modulation indirect loss : in, This marks the end of a single frequency modulation phase. Calculate the indirect loss of second frequency modulation : in, This marks the end of the second frequency modulation phase. Calculate fault status Total indirect losses : Computational system-level indirect power shortage expectation : in, System fault status The probability of occurrence, This represents the number of fault states.

10. The method for assessing the operational reliability of a wind-storage integrated system considering frequency stability constraints according to claim 8, characterized in that, S4 specifically includes: The set of anticipated failure scenarios specifically includes: Input the reference system parameters and set the quantization boundary for safe frequency operation, and generate a set of anticipated fault scenarios based on deterministic criteria or probabilistic sampling methods; The process of sequentially performing frequency dynamic security checks and reliability consequence assessments for each fault state specifically includes: For each fault in the set of anticipated fault scenarios, the dynamic power support of the wind-storage combined system is simulated based on the power deficit model established in step S1. The frequency dynamic equation in step S2 is substituted to solve the frequency time-domain response curve, and the lowest frequency point and steady-state value are extracted and a safety check is performed. Substitute the frequency time-domain response curve into the model in step S3 to calculate the instantaneous trip probability of each synchronous generator and correct its equivalent failure rate. Perform optimal load reduction calculation to obtain the direct load shedding amount, and calculate the indirect energy loss at the same time. The method of calculating the expected system-level power shortage and the expected indirect power shortage through full-state probability aggregation, and outputting the evaluation results, specifically includes: Calculate the probability of occurrence of each fault state, and aggregate the expected power shortage at the system level and the expected power shortage in the indirect.

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