A multi-region power system two-stage distribution robust unit commitment method and system considering wind condition related wind turbine inertia uncertainty

Abstract

This invention discloses a two-stage sub-Brønsted bar unit combination method and system for a multi-regional power system considering the uncertainty of wind condition-related wind turbine inertia. The method includes: acquiring multi-regional power system operation data; establishing a multi-regional power system operation model based on the multi-regional power system operation data; establishing a partitioned inertia model, representing available inertia as the sum of synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia, and tie-line support inertia; establishing a wind turbine inertia uncertainty model, obtaining wind condition-related wind turbine equivalent inertia estimates based on wind speed prediction data, and constructing a sub-Brønsted bar fuzzy set of wind turbine inertia prediction errors; establishing a two-stage sub-Brønsted bar unit combination model based on the multi-regional power system operation model, the partitioned inertia model, and the wind turbine inertia uncertainty model; transforming the two-stage sub-Brønsted bar unit combination model to obtain a mixed integer linear programming model and solving it, outputting the unit combination results and partitioned inertia safety scheduling results.

Description

Technical Field

[0001] This invention relates to the field of power system optimization scheduling and frequency security control technology, and in particular to a two-stage split-blown rod unit combination method and system for multi-regional power systems that considers the uncertainty of wind turbine inertia related to wind conditions. Background Technology

[0002] With the advancement of energy structure transformation and the construction of new power systems, the proportion of new energy units such as wind power and photovoltaic power in the power system is constantly increasing. Unlike traditional synchronous generators, wind turbines are usually connected to the grid through power electronic converters. Their rotor kinetic energy is no longer naturally coupled with the system frequency, resulting in a decrease in the available inertia level of the power system. When disturbances such as large-capacity unit tripping, regional tie-line faults, or sudden load changes occur in the system, the system frequency change rate may increase rapidly, and the frequency minimum point may exceed the limit, thereby increasing the risk of low-frequency load shedding, equipment disconnection from the grid, and even cascading failures.

[0003] Unit combination is a crucial tool for day-ahead operation and dispatching in power systems. Traditional unit combination models typically aim to minimize operating costs and consider constraints such as unit start-up and shutdown, output limits, ramp-up, power balance, and reserve. With the increasing integration of renewable energy, existing methods are gradually incorporating frequency security indicators into unit combination models, such as system inertia, rate of frequency change, frequency minimum, and steady-state frequency deviation. However, existing frequency-constrained unit combination methods still have the following shortcomings:

[0004] First, the inertia of wind turbines is often simplified to a fixed or deterministic parameter. In reality, the equivalent inertia that a wind turbine can provide is closely related to wind speed, rotor speed, converter control strategy, and measurement and communication delays. Under different wind conditions, the kinetic energy that the turbine rotor can release and its virtual inertia support capacity vary significantly, and using a fixed inertia parameter may lead to inaccurate inertia assessments.

[0005] Second, existing methods often focus on whether the total system inertia meets the requirements, while neglecting the differences in the spatial distribution of inertia in multi-regional power systems. In multi-regional interconnected systems, even if the total system inertia meets the network-wide requirements, some local areas may still experience insufficient inertia due to concentrated access of new energy sources, insufficient operation of synchronous machines, limited power flow on tie lines, or tie line faults. In such cases, relying solely on system-level inertia constraints is insufficient to guarantee frequency security in each region.

[0006] Third, existing uncertainty dispatch methods primarily focus on the uncertainties of wind power output, load demand, or reserve call, while insufficiently considering the uncertainties inherent in wind turbine inertia. Wind turbine inertia prediction errors exhibit significant spatiotemporal heterogeneity; the error distribution may differ significantly across different time windows, wind farms, and wind speed ranges. Using a single probability distribution or a static uncertainty set makes it difficult to accurately characterize the uncertainty of wind turbine inertia. Summary of the Invention

[0007] Purpose of the invention: The purpose of this invention is to provide a two-stage blobsed turbine unit combination method and system for multi-regional power systems that considers the uncertainty of wind turbine inertia related to wind conditions. This method enables the regional inertia safety constraint scheduling of multi-regional power systems when the turbine inertia varies with wind conditions and the probability distribution is unknown. It solves the problems of insufficient local inertia and insufficient consideration of the uncertainty of wind turbine inertia in existing unit combination models.

[0008] Technical solution: The present invention provides a two-stage split-blown rod turbine combination method for multi-regional power systems considering the uncertainty of wind condition-related wind turbine inertia, comprising:

[0009] Acquire operational data of power systems in multiple regions;

[0010] Based on multi-regional power system operation data, a multi-regional power system operation model is established. The multi-regional power system operation model includes a thermal power unit operation model, an energy storage device operation model, a system power balance model, a regional power balance model, and a regional tie-line power constraint model.

[0011] Establish a zoned inertia model, representing the available inertia of each region at each time period as the sum of synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia, and tie line support inertia, and determine the minimum inertia requirement of the corresponding region based on the maximum active power disturbance and the maximum allowable frequency change rate of the region.

[0012] An uncertainty model for wind turbine inertia is established. Based on wind speed prediction data, the estimated equivalent inertia of wind turbines related to wind conditions is obtained. Based on historical prediction error samples, a fuzzy set of wind turbine inertia prediction errors is constructed.

[0013] Based on the aforementioned multi-regional power system operation model, zonal inertia model, and wind turbine inertia uncertainty model, a two-stage sparse blower unit combination model is established. In the first stage, the daytime start-up and shutdown status of the units, the output of the thermal power units, the energy storage charging and discharging power, and the regional tie line power are determined based on the wind turbine inertia estimate. In the second stage, after the wind turbine inertia prediction error is realized, the synchronous machine inertia, energy storage inertia, and tie line support inertia are readjusted.

[0014] The two-stage sub-Blu-rod unit combination model is transformed to obtain a mixed integer linear programming model, which is then solved. The results of unit combination, power dispatch, energy storage operation, tie-line transmission, and regional inertia safety verification for the multi-regional power system are output.

[0015] Furthermore, the thermal power unit operation model includes the thermal power unit's fuel cost, start-up and shutdown costs, upper and lower output limits, and ramping constraints. The thermal power unit's fuel cost is expressed as:

[0016]

[0017] The start-up and shutdown costs of thermal power units are expressed as follows:

[0018]

[0019] The upper and lower limits of thermal power unit output constraints are expressed as follows:

[0020]

[0021] The ramp-up constraint for thermal power units is expressed as:

[0022]

[0023] The downhill ramp constraint of a thermal power unit is expressed as:

[0024]

[0025]

[0026] The output constraints of renewable energy units are expressed as follows:

[0027] In the formula, Let i be the fuel cost of thermal power unit i in time period t. Let i be the start-up and shutdown cost of thermal power unit i during time period t. To provide power to thermal power units, These are the start-up and shutdown status variables for thermal power units. This is the fuel cost coefficient. and These are startup costs and downtime costs, respectively. These are the lower and upper limits of thermal power unit output, respectively. These are the uphill and downhill ramp limits for thermal power units, respectively. Powering renewable energy units These represent the lower and upper limits of output for renewable energy units, respectively. For the set of scheduling periods, This refers to a collection of thermal power units.

[0028] Furthermore, the energy storage device operation model includes energy storage degradation cost, energy state constraints, energy dynamic update constraints, first-to-last energy balance constraints, and charging / discharging power mutual exclusion constraints.

[0029] The energy storage degradation cost is expressed as:

[0030]

[0031] Energy storage state constraints are expressed as follows:

[0032]

[0033] The constraints for dynamic updating of energy storage are expressed as follows:

[0034]

[0035] The energy balance constraint of energy storage is expressed as follows:

[0036]

[0037] The mutual exclusion constraint of energy storage charging and discharging power is expressed as:

[0038]

[0039] In the formula, The degradation cost of energy storage device j in time period t. This is the energy storage degradation cost coefficient. and These are the energy storage charging power and discharging power, respectively. and These refer to energy storage charging efficiency and discharging efficiency, respectively. In terms of energy storage state, and These are the lower and upper limits of energy storage capacity, respectively. For energy storage charging and discharging state variables, For the set of scheduling periods, It is a collection of energy storage devices.

[0040] Furthermore, the system power balance model, the zone power balance model, and the tie-line power constraint model include:

[0041] Regional link line transmission cost:

[0042]

[0043] System power balance constraints:

[0044]

[0045] Zoned power balance constraints:

[0046]

[0047] Tie line power transmission constraints:

[0048]

[0049] In the formula, Let be the transmission cost of the link between region m and region n during time period t. This is the transmission cost factor for the tie line. Let m be the power transferred from region m to region n. For load power, For a set of regions, Let m be the set of regions adjacent to region m. Let m be the set of wind turbine units within region m. This refers to the collection of energy storage devices within region m. For the load set within region m, For a set of nodes, Let be the set of nodes connected to node q. For load sets.

[0050] Furthermore, the partitioned inertia model includes minimum inertia requirement constraints for partitions, total inertia composition constraints for partitions, synchronous machine inertia constraints, virtual inertia constraints, wind turbine virtual inertia constraints, energy storage inertia constraints, and tie line support inertia constraints.

[0051] The minimum inertia requirement constraint for each partition is expressed as:

[0052]

[0053] The constraint for the total inertia of the partition is expressed as follows:

[0054]

[0055] The inertia constraint of the synchronous machine is expressed as:

[0056]

[0057] The virtual inertia constraint is expressed as:

[0058]

[0059] The virtual inertia constraint of wind turbine units is expressed as:

[0060]

[0061] The virtual inertia constraint for energy storage is expressed as:

[0062]

[0063] The inertia constraint of the connecting line support is expressed as:

[0064]

[0065] In the formula, Let m be the total available inertia of region m during time period t. Let m be the maximum active power disturbance in region m during time period t. The maximum permissible rate of frequency change in the region. For the inertia of the synchronous machine, For virtual inertia, To support the inertia of the connecting line, Let be the inertia constant of thermal power unit i. For the inertia of the wind turbine, For energy storage inertia.

[0066] Furthermore, the wind turbine inertia uncertainty model includes the sum of the wind turbine equivalent inertia estimate and the random variable of wind turbine inertia prediction error.

[0067] Furthermore, the partial Bruker fuzzy set of wind turbine inertia prediction error is constructed based on historical prediction error samples formed by different time windows and different wind power clusters. The partial Bruker fuzzy set of wind turbine inertia prediction error is represented as follows:

[0068]

[0069] In the formula, For the fuzzy set of wind turbine inertia prediction error, Let be the probability distribution of the random variable representing the wind turbine inertia prediction error. As a set of indices representing different spatiotemporal operating states, Let be the probability distribution of the random variable representing the wind turbine inertia prediction error for the k-th type of operating state. The mixed weights are for the k-th class of running states. Let k be the empirical distribution formed by the error samples of the kth class. Centered on the empirical distribution with a radius of Wasserstein's ball.

[0070] Furthermore, the estimated equivalent inertia of the wind turbine is calculated segment by segment based on the wind speed operating range, and is expressed as follows:

[0071]

[0072] In the formula, Let v be the equivalent inertia of the wind turbine at wind speed v. These are the differential coefficients for virtual inertia control. The initial synchronization angular velocity of the system, The rated speed of the wind turbine. The time constant of the low-pass filter. The converter response time constant, The equivalent virtual inertia delay coefficient, The coefficients corresponding to the maximum power point tracking interval are... This is the virtual inertia control suppression coefficient. , , These are the cut-in wind speed, the turning wind speed in the maximum power point tracking interval, and the turning wind speed in the constant power interval, respectively.

[0073] Furthermore, the two-stage split-blown rod unit combination model takes the minimum sum of thermal power fuel cost, thermal power start-up and shutdown cost, energy storage degradation cost, tie-line transmission cost, and expected inertia adjustment cost under the worst-case distribution as its objective function:

[0074]

[0075] Its constraints include thermal power unit operation constraints, energy storage device operation constraints, system power balance constraints, zonal power balance constraints, tie line power transmission constraints, and zonal inertia constraints of the blobs.

[0076] The formula for the two-stage split-blown rod unit combination model is as follows:

[0077]

[0078]

[0079] Where x is the decision variable for the first stage. For the cost of second-stage inertia readjustment, This is due to the fan inertia deviation. Indicates normal operating constraints. Available inertia for partitioning, Minimum inertia requirement for partitioning.

[0080] A two-stage split-blown bar turbine combination system for multi-regional power systems, considering the uncertainty of wind condition-dependent turbine inertia, includes:

[0081] The data input and storage module is used to input and store multi-regional power system operation data;

[0082] The operational constraint modeling module is used to establish operating models for thermal power units, energy storage devices, system power balance, regional power balance, and regional tie-line power constraint models based on multi-regional power system operating data.

[0083] The partitioned inertia modeling module is used to establish a partitioned inertia model, which represents the available inertia of each region in each time period as the sum of the synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia and tie line support inertia, and determines the minimum inertia requirement of the corresponding region based on the maximum active power disturbance and the maximum allowable frequency change rate of the region.

[0084] The wind turbine inertia uncertainty modeling module establishes a wind turbine inertia uncertainty model, obtains wind condition-related equivalent inertia estimates based on wind speed prediction data, and constructs a fuzzy set of wind turbine inertia prediction errors based on historical prediction error samples.

[0085] The two-stage optimization module is used to establish a two-stage blobs unit combination model based on the multi-regional power system operation model, the zonal inertia model, and the wind turbine inertia uncertainty model. The first stage determines the day-ahead unit start-up and shutdown status, thermal power unit output, energy storage charging and discharging power, and regional tie line power based on the wind turbine inertia estimate. The second stage readjusts the synchronous machine inertia, energy storage inertia, and tie line support inertia after the wind turbine inertia prediction error is realized.

[0086] The model transformation and solution module is used to transform the two-stage split-bulb unit combination model to obtain a mixed integer linear programming model and solve it.

[0087] The results output module is used to output the unit combination results, power dispatch results, energy storage operation results, tie line transmission results, and zonal inertia safety verification results of the multi-regional power system.

[0088] An electronic device is characterized by comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement a two-stage sub-blown bar turbine combination method for a multi-regional power system considering the uncertainty of wind condition-related wind turbine inertia.

[0089] A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the aforementioned two-stage split-blown rod unit combination method for a multi-regional power system considering the uncertainty of wind condition-related wind turbine inertia.

[0090] Beneficial Effects: Compared with the prior art, the present invention has the following significant advantages: 1. By modeling the equivalent inertia of the wind turbine as a wind condition-related variable, it can reflect the differences in the virtual inertia support capacity of the wind turbine unit under different wind speed operating ranges, avoiding safety assessment deviations caused by fixed inertia parameters; 2. By extending the total system inertia constraint to a regional inertia constraint, it can identify local inertia insufficiency problems in multi-regional power systems caused by uneven distribution of new energy sources, insufficient synchronous machine operation, limited tie lines, or tie line faults; 3. Based on the convex combination of multiple Wasserstein spheres, a partial Bruker bar model of wind turbine inertia prediction error is constructed. Fuzzy sets can characterize the spatiotemporal heterogeneity of prediction errors under different time windows and different wind power clusters, improving the adaptability and robustness of inertia uncertainty modeling; 4. A two-stage sub-Blu-ray bar scheduling framework is adopted. The first stage determines the day-ahead unit combination and power plan, and the second stage readjusts according to the inertia uncertainty of wind turbines, which reduces the economic losses caused by overly conservative scheduling while ensuring the safety of zoned frequencies; 5. The original sub-Blu-ray bar chance constraint model is transformed into a solvable mixed integer linear programming model through dual transformation and conditional risk value approximation, which is convenient for embedding into existing scheduling optimization platforms and energy management systems. Attached Figure Description

[0091] Figure 1 A framework diagram of the method of this invention. Detailed Implementation

[0092] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0093] like Figure 1 As shown, the present invention provides a two-stage split-blown bar turbine combination method for multi-regional power systems that considers the uncertainty of wind condition-related wind turbine inertia, comprising:

[0094] (1) Obtain multi-regional power system operation data, wherein the multi-regional power system operation data includes a set of thermal power units. Collection of thermal power units within region m Wind turbine assembly Wind turbine set within region m Energy storage device collection Region set Set of scheduling periods Adjacent regions of each area Load forecast The data includes wind speed forecast value v, historical wind speed samples, thermal power unit cost parameters, energy storage parameters, tie line capacity parameters, and frequency safety parameters.

[0095] (2) Based on the operating data, establish a multi-regional power system operation model, which includes a thermal power unit operation model, an energy storage device operation model, a system power balance model, a regional power balance model, and a regional tie-line power constraint model;

[0096] The thermal power unit operation model includes the thermal power unit's fuel cost, start-up and shutdown cost, upper and lower output limits, and ramping constraints. The thermal power unit's fuel cost is expressed as:

[0097] (1)

[0098] The start-up and shutdown costs of thermal power units are expressed as follows:

[0099] (2)

[0100] The upper and lower limits of thermal power unit output constraints are expressed as follows:

[0101] (3)

[0102] The ramping constraint for thermal power units is expressed as:

[0103] (4)

[0104] The downhill ramp constraint of a thermal power unit is expressed as:

[0105] (5)

[0106] The output constraints of renewable energy units are expressed as follows:

[0107] (6)

[0108] In the formula, Let i be the fuel cost of thermal power unit i in time period t. Let i be the start-up and shutdown cost of thermal power unit i during time period t. To provide power to thermal power units, These are the start-up and shutdown status variables for thermal power units. This is the fuel cost coefficient. and These are startup costs and downtime costs, respectively. These are the lower and upper limits of thermal power unit output, respectively. These are the uphill and downhill ramp limits for thermal power units, respectively. Powering renewable energy units These are the lower and upper limits of the output of renewable energy units, respectively.

[0109] The energy storage device operation model includes energy storage degradation cost, energy state constraints, energy dynamic update constraints, first and last energy balance constraints, and charging and discharging power mutual exclusion constraints.

[0110] The energy storage degradation cost is expressed as:

[0111] (7)

[0112] Energy storage state constraints are expressed as follows:

[0113] (8)

[0114] The constraints for dynamic updating of energy storage are expressed as follows:

[0115] (9)

[0116] The energy balance constraint of energy storage is expressed as follows:

[0117] (10)

[0118] The mutual exclusion constraint of energy storage charging and discharging power is expressed as:

[0119] (11)

[0120] In the formula, The degradation cost of energy storage device j in time period t. This is the energy storage degradation cost coefficient. and These are the energy storage charging power and discharging power, respectively. and These refer to energy storage charging efficiency and discharging efficiency, respectively. In terms of energy storage state, and These are the lower and upper limits of energy storage capacity, respectively. For energy storage charging and discharging state variables;

[0121] The system power balance model, the zone power balance model, and the tie-line power constraint model include:

[0122] The transmission cost of a regional tie line is expressed as follows:

[0123] (12)

[0124] The system power balance constraint is expressed as:

[0125] (13)

[0126] The partitioned power balance constraint is expressed as:

[0127] (14)

[0128] The tie-line power transmission constraint is expressed as:

[0129] (15)

[0130] In the formula, Let be the transmission cost of the link between region m and region n during time period t. This is the transmission cost factor for the tie line. Let m be the power transferred from region m to region n. For load power, For a set of regions, Let m be the set of regions adjacent to region m.

[0131] (3) Establish a zone inertia model, and express the available inertia of each region in each time period as the sum of synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia and tie line support inertia, and determine the minimum inertia requirement of the corresponding region based on the maximum active disturbance and the maximum allowable frequency change rate of the region.

[0132] The partitioned inertia model includes constraints on minimum inertia requirement for each partition, constraints on total inertia composition for each partition, constraints on synchronous machine inertia, constraints on virtual inertia, constraints on virtual inertia of wind turbine units, constraints on energy storage inertia, and constraints on tie line support inertia.

[0133] The minimum inertia requirement constraint for each partition is expressed as:

[0134] (16)

[0135] The constraint for the total inertia of the partition is expressed as follows:

[0136] (17)

[0137] The inertia constraint of the synchronous machine is expressed as:

[0138] (18)

[0139] The virtual inertia constraint is expressed as:

[0140] (19)

[0141] The virtual inertia constraint of wind turbine units is expressed as:

[0142] (20)

[0143] The virtual inertia constraint for energy storage is expressed as:

[0144] (twenty one)

[0145] The inertia constraint of the connecting line support is expressed as:

[0146] (twenty two)

[0147] In the formula, Let m be the total available inertia of region m during time period t. Let m be the maximum active power disturbance in region m during time period t. The maximum permissible rate of frequency change in the region. For the inertia of the synchronous machine, For virtual inertia, To support the inertia of the connecting line, Let be the inertia constant of thermal power unit i. For the inertia of the wind turbine, For energy storage inertia;

[0148] Among them, based on the maximum possible active disturbance in each region and the maximum permissible frequency change rate of the region The minimum inertia requirement for the region is calculated using formula (16); the available inertia for the region is calculated using formulas (17) to (22). The synchronous machine inertia is contributed by the operating thermal power units in the region; the virtual inertia of the wind turbines is contributed by the equivalent inertia of the wind turbines in the region under the current wind conditions; the virtual inertia of the energy storage is contributed by the rapid power support capability of the energy storage device; and the tie line support inertia is equivalently obtained by the active power response provided by the adjacent regions through the tie line.

[0149] (4) Establish a model for the uncertainty of wind turbine inertia, obtain the estimated value of the equivalent inertia of wind turbines related to wind conditions based on wind speed prediction data, and construct a fuzzy set of wind turbine inertia prediction error based on historical prediction error samples;

[0150] The inertia of a wind turbine is significantly affected by wind speed, rotor speed, and converter control characteristics, exhibiting a clear correlation with wind conditions. An uncertainty model of wind turbine inertia expresses the equivalent inertia of the turbine as the sum of the estimated value and the random variable of the wind turbine inertia prediction error:

[0151] (twenty three)

[0152] In the formula, The equivalent inertia of the wind turbine. This is an estimate of the equivalent inertia of the wind turbine. Let be the random variable representing the prediction error of wind turbine inertia. The wind turbine inertia uncertainty model includes the equivalent inertia of the wind turbine, which is a definite estimate. The wind turbine inertia uncertainty model models the wind turbine inertia as the sum of a definite estimate and an uncertain random error.

[0153] To address uncertainty, an affine adjustment strategy is adopted for the inertia of the synchronizing machine, energy storage, and tie lines. Based on this strategy, the actual adjustment forms of the synchronizing machine inertia, the virtual inertia of the energy storage, and the tie line support inertia are expressed as follows:

[0154] (twenty four)

[0155] (25)

[0156] (26)

[0157] The corresponding inertia adjustment cost is expressed as:

[0158] (27)

[0159] In the formula, , , These are the factors contributing to the prediction error of wind turbine inertia, namely, the inertia of the synchronous machine, energy storage, and tie line support. , , These are the corresponding inertia adjustment cost coefficients;

[0160] Since the true probability distribution of wind turbine inertia error is unknown, a partial Bruker chance constraint is constructed based on the wind turbine inertia uncertainty model to ensure that, under all permissible probability distributions, the probability of each region meeting the minimum inertia requirement is not less than a given confidence level.

[0161] (28)

[0162] In the formula, This represents the unknown probability distribution of the wind turbine inertia prediction error. Let m be the fuzzy set of wind turbine inertia uncertainty in region m during time period t. This is the risk tolerance parameter.

[0163] Based on historical wind speed samples and estimated equivalent inertia of wind turbines, inertia prediction error samples under multiple spatiotemporal operating conditions are obtained. An empirical distribution is constructed for each type of sample, and a Wasserstein sphere is constructed with this empirical distribution as the center. Then, multiple Wasserstein spheres are combined convexly according to weights to obtain the fuzzy set of the sub-Brussels bar as shown in formula (29).

[0164] The sub-Bruker bar fuzzy set of wind turbine inertia prediction error is constructed based on historical prediction error samples formed by different time windows and different wind power clusters. Multiple empirical distributions are constructed, and a Wasserstein sphere is established with the empirical distribution as the center. The sub-Bruker bar fuzzy set of wind turbine inertia prediction error is represented as follows:

[0165] (29)

[0166] And the induced distribution set of the equivalent inertia of the wind turbine is obtained through affine mapping:

[0167] (30)

[0168] In the formula, To divide the fuzzy set of the Blue bar, As a set of indices representing different spatiotemporal operating states, The mixed weights are for the k-th class of running states. Let k be the empirical distribution formed by the error samples of the kth class. Centered on the empirical distribution with a radius of Wasserstein ball, For probability distribution operators of random variables, This represents the probability distribution of the wind turbine's inertia.

[0169] The fuzzy set of wind turbine inertia prediction error is described by a convex combination of multiple Wasserstein spheres to describe the difference in prediction error distribution under different operating conditions. This avoids treating wind turbine inertia errors under different spatiotemporal conditions as equal and improves the adaptability of inertia uncertainty characterization.

[0170] The estimated equivalent inertia of the wind turbine is calculated segment by segment based on the wind speed operating range, and is expressed as follows:

[0171] (31)

[0172] In the formula, Let v be the equivalent inertia of the wind turbine at wind speed v. These are the differential coefficients for virtual inertia control. The initial synchronization angular velocity of the system, The rated speed of the wind turbine. The time constant of the low-pass filter. The converter response time constant, The equivalent virtual inertia delay coefficient, The coefficients corresponding to the maximum power point tracking interval are... This is the virtual inertia control suppression coefficient. , , These are the cut-in wind speed, the turning wind speed in the maximum power point tracking interval, and the turning wind speed in the constant power interval, respectively.

[0173] Formula (31) can map the effects of wind speed changes on the rotor speed, frequency response, and virtual inertia release capability of the wind turbine to the estimated value of the equivalent inertia.

[0174] For wind turbines in the range of maximum power point tracking, maximum power point tracking to constant speed, and constant speed to constant power, corresponding equivalent inertia expressions are used. This step enables the mapping of wind speed to wind turbine rotor speed, frequency response power, and equivalent inertia.

[0175] (5) Based on the multi-regional power system operation model, the zonal inertia model and the wind turbine inertia uncertainty model, a two-stage spooled rod unit combination model is established. In the first stage, the daytime start-up and shutdown status of the unit, the output of the thermal power unit, the energy storage charging and discharging power and the regional tie line power are determined according to the wind turbine inertia estimate. In the second stage, after the wind turbine inertia prediction error is realized, the synchronous machine inertia, energy storage inertia and tie line support inertia are readjusted. The readjustment cost is expressed by formula (27).

[0176] The two-stage stub-type generator unit combination model takes the minimum sum of thermal power fuel cost, thermal power start-up and shutdown cost, energy storage degradation cost, tie-line transmission cost, and the expected inertia adjustment cost under the worst-case distribution as its objective function. This means that, under the premise of satisfying conventional operation constraints and zonal inertia safety constraints, it minimizes the sum of the first-stage operating cost and the second-stage expected inertia adjustment cost under the worst-case distribution. The two-stage objective function is:

[0177] (32)

[0178] Its constraints include thermal power unit operation constraints, energy storage device operation constraints, system power balance constraints, zonal power balance constraints, tie line power transmission constraints, and zonal inertia constraints of the blobs.

[0179] The two-stage split-blown bar unit combination model can be written as:

[0180] (33)

[0181] (34)

[0182] Where x represents the decision variables for the first stage, including the unit start-up and shutdown variables, thermal power unit output variables, energy storage charging and discharging variables, and tie-line power variables. For the cost of second-stage inertia readjustment, This is due to the fan inertia deviation. Indicates normal operating constraints. Available inertia for partitioning, Minimum inertia requirement for partitioning.

[0183] (6) Transform the two-stage sub-blob unit combination model to obtain a mixed integer linear programming model and solve it, and output the unit combination results, power dispatch results, energy storage operation results, tie line transmission results and regional inertia safety verification results of the multi-regional power system.

[0184] For the worst-case expectation term, we can use duality theory to transform it:

[0185] (35)

[0186] For the split-stakes opportunity constraint, conditional value at risk is used as an approximation, and auxiliary variables are introduced. and :

[0187] (36)

[0188] (37)

[0189] By transforming formula (35) into formula (37), the original two-stage split-blown unit combination model can be transformed into a mixed integer linear programming model, and solved using a commercial solver or an open-source solver.

[0190] Since the original model contains worst-case expectation and split-Brow chance constraints, it is difficult to solve directly. The embodiment uses dual theory to transform the worst-case expectation term into a deterministic expression as shown in formula (35); and uses conditional risk value approximation to transform the split-Brow chance constraints into linear constraints as shown in formulas (36) to (37); finally, a solvable mixed-integer linear programming model is obtained.

[0191] Solving this model using Gurobi, CPLEX, or other mixed-integer linear programming solvers will yield the unit combination results and the zonal inertia safety scheduling results: the start-up and shutdown status of thermal power units in each time period. Thermal power unit output Wind power reception capacity Energy storage charging power Energy storage and discharge power Energy storage energy status Regional tie line power Inertia of the synchronizing machines in each area Fan inertia Energy storage inertia Connection line support inertia Total inertia of the region And partition inertia safety margin.

[0192] This invention provides a two-stage distributed broiler unit combination system for multi-regional power systems that considers the uncertainty of wind turbine inertia related to wind conditions. The system includes a data input and storage module, an operational constraint modeling module, a regional inertia modeling module, a wind turbine inertia uncertainty modeling module, a two-stage optimization module, a model transformation and solution module, and a result output module.

[0193] The data input and storage module is used to input and store multi-regional power system operation data;

[0194] The operational constraint modeling module is used to establish operating models for thermal power units, energy storage devices, system power balance, regional power balance, and regional tie-line power constraint models based on multi-regional power system operating data.

[0195] The partitioned inertia modeling module is used to establish a partitioned inertia model, which represents the available inertia of each region in each time period as the sum of the synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia and tie line support inertia, and determines the minimum inertia requirement of the corresponding region based on the maximum active power disturbance and the maximum allowable frequency change rate of the region.

[0196] The wind turbine inertia uncertainty modeling module establishes a wind turbine inertia uncertainty model, obtains wind condition-related equivalent inertia estimates based on wind speed prediction data, and constructs a fuzzy set of wind turbine inertia prediction errors based on historical prediction error samples.

[0197] The two-stage optimization module is used to establish a two-stage blobs unit combination model based on the multi-regional power system operation model, the zonal inertia model, and the wind turbine inertia uncertainty model. The first stage determines the day-ahead unit start-up and shutdown status, thermal power unit output, energy storage charging and discharging power, and regional tie line power based on the wind turbine inertia estimate. The second stage readjusts the synchronous machine inertia, energy storage inertia, and tie line support inertia after the wind turbine inertia prediction error is realized.

[0198] The model transformation and solution module is used to transform the two-stage split-bulb unit combination model to obtain a mixed integer linear programming model and solve it.

[0199] The results output module is used to output the unit combination results, power dispatch results, energy storage operation results, tie line transmission results, and zonal inertia safety verification results of the multi-regional power system.

[0200] This invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements a two-stage split-blown bar turbine unit combination method for a multi-regional power system that considers the uncertainty of wind condition-related wind turbine inertia.

[0201] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a two-stage split-blown rod unit combination method for a multi-regional power system considering the uncertainty of wind turbine inertia related to wind conditions.

Claims

1. A two-stage split-blown bar turbine unit combination method for a multi-regional power system considering the uncertainty of wind condition-dependent wind turbine inertia, characterized in that, Includes the following steps: Acquire operational data of power systems in multiple regions; Based on multi-regional power system operation data, a multi-regional power system operation model is established. The multi-regional power system operation model includes a thermal power unit operation model, an energy storage device operation model, a system power balance model, a regional power balance model, and a regional tie-line power constraint model. Establish a zoned inertia model, representing the available inertia of each region at each time period as the sum of synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia, and tie line support inertia, and determine the minimum inertia requirement of the corresponding region based on the maximum active power disturbance and the maximum allowable frequency change rate of the region. An uncertainty model for wind turbine inertia is established. Based on wind speed prediction data, the estimated equivalent inertia of wind turbines related to wind conditions is obtained. Based on historical prediction error samples, a fuzzy set of wind turbine inertia prediction errors is constructed. Based on the aforementioned multi-regional power system operation model, zonal inertia model, and wind turbine inertia uncertainty model, a two-stage sparse blower unit combination model is established. In the first stage, the daytime start-up and shutdown status of the units, the output of the thermal power units, the energy storage charging and discharging power, and the regional tie line power are determined based on the wind turbine inertia estimate. In the second stage, after the wind turbine inertia prediction error is realized, the synchronous machine inertia, energy storage inertia, and tie line support inertia are readjusted. The two-stage sub-Blu-rod unit combination model is transformed to obtain a mixed integer linear programming model, which is then solved. The results of unit combination, power dispatch, energy storage operation, tie-line transmission, and regional inertia safety verification for the multi-regional power system are output.

2. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The thermal power unit operation model includes the thermal power unit's fuel cost, start-up and shutdown cost, upper and lower output limits, and ramping constraints. The thermal power unit's fuel cost is expressed as: The start-up and shutdown costs of thermal power units are expressed as follows: The upper and lower limits of thermal power unit output constraints are expressed as follows: The ramp-up constraint for thermal power units is expressed as: The downhill ramp constraint of a thermal power unit is expressed as: The output constraints of renewable energy units are expressed as follows: In the formula, Let i be the fuel cost of thermal power unit i in time period t. Let i be the start-up and shutdown cost of thermal power unit i during time period t. To provide power to thermal power units, These are the start-up and shutdown status variables for thermal power units. This is the fuel cost coefficient. and These are startup costs and downtime costs, respectively. These are the lower and upper limits of thermal power unit output, respectively. These are the uphill and downhill ramp limits for thermal power units, respectively. Powering renewable energy units These represent the lower and upper limits of output for renewable energy units, respectively. For the set of scheduling periods, This refers to a collection of thermal power units.

3. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The energy storage device operation model includes energy storage degradation cost, energy state constraints, energy dynamic update constraints, first and last energy balance constraints, and charging and discharging power mutual exclusion constraints. The energy storage degradation cost is expressed as: Energy storage state constraints are expressed as follows: The constraints for dynamic updating of energy storage are expressed as follows: The energy balance constraint of energy storage is expressed as follows: The mutual exclusion constraint of energy storage charging and discharging power is expressed as: In the formula, The degradation cost of energy storage device j in time period t. This is the energy storage degradation cost coefficient. and These are the energy storage charging power and discharging power, respectively. and These refer to energy storage charging efficiency and discharging efficiency, respectively. In terms of energy storage state, and These are the lower and upper limits of energy storage capacity, respectively. For energy storage charging and discharging state variables, For the set of scheduling periods, It is a collection of energy storage devices.

4. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The system power balance model, the zone power balance model, and the tie-line power constraint model include: Regional link transmission cost: System power balance constraints: Zoned power balance constraints: Tie line power transmission constraints: In the formula, Let be the transmission cost of the link between region m and region n during time period t. This is the transmission cost factor for the tie line. Let m be the power transferred from region m to region n. For load power, For a set of regions, Let m be the set of regions adjacent to region m. Let m be the set of wind turbine units within region m. This refers to the collection of energy storage devices within region m. For the load set within region m, For a set of nodes, Let be the set of nodes connected to node q. For load sets.

5. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The partitioned inertia model includes constraints on minimum inertia requirement for each partition, constraints on total inertia composition for each partition, constraints on synchronous machine inertia, constraints on virtual inertia, constraints on virtual inertia of wind turbine units, constraints on energy storage inertia, and constraints on tie line support inertia. The minimum inertia requirement constraint for each partition is expressed as: The constraint for the total inertia of the partition is expressed as follows: The inertia constraint of the synchronous machine is expressed as: The virtual inertia constraint is expressed as: The virtual inertia constraint of wind turbine units is expressed as: The virtual inertia constraint for energy storage is expressed as: The inertia constraint of the connecting line support is expressed as: In the formula, Let m be the total available inertia of region m during time period t. Let m be the maximum active power disturbance in region m during time period t. The maximum permissible rate of frequency change in the region. For the inertia of the synchronous machine, For virtual inertia, To support the inertia of the connecting line, Let be the inertia constant of thermal power unit i. For the inertia of the wind turbine, For energy storage inertia.

6. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The uncertainty model of wind turbine inertia includes the sum of the estimated equivalent inertia of the wind turbine and the random variable of the wind turbine inertia prediction error.

7. The method for combining two-stage split-blown rod generator units in a multi-regional power system according to claim 1, characterized in that, The sub-Blule bar fuzzy set of wind turbine inertia prediction error is constructed based on historical prediction error samples formed by different time windows and different wind power clusters. The sub-Blule bar fuzzy set of wind turbine inertia prediction error is represented as follows: In the formula, For the fuzzy set of wind turbine inertia prediction error, Let be the probability distribution of the random variable representing the wind turbine inertia prediction error. As a set of indices representing different spatiotemporal operating states, Let be the probability distribution of the random variable representing the wind turbine inertia prediction error for the k-th type of operating state. The mixed weights are for the k-th class of running states. Let k be the empirical distribution formed by the error samples of the kth class. Centered on the empirical distribution with a radius of Wasserstein's ball.

8. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The estimated equivalent inertia of the wind turbine is calculated segment by segment based on the wind speed operating range, and is expressed as follows: In the formula, Let v be the equivalent inertia of the wind turbine at wind speed v. These are the differential coefficients for virtual inertia control. The initial synchronization angular velocity of the system, The rated speed of the wind turbine. The time constant of the low-pass filter. The converter response time constant, The equivalent virtual inertia delay coefficient, The coefficients corresponding to the maximum power point tracking interval are... This is the virtual inertia control suppression coefficient. , , These are the cut-in wind speed, the turning wind speed in the maximum power point tracking interval, and the turning wind speed in the constant power interval, respectively.

9. The two-stage split-blown rod generator combination method for multi-regional power systems according to claim 1, characterized in that, The two-stage split-blown rod unit combination model takes minimizing the sum of thermal power fuel cost, thermal power start-up and shutdown cost, energy storage degradation cost, tie-line transmission cost, and expected inertia regulation cost under the worst-case distribution as its objective function: Its constraints include thermal power unit operation constraints, energy storage device operation constraints, system power balance constraints, zone power balance constraints, tie line power transmission constraints, and zone inertia constraints of the blobs. The formula for the two-stage split-blown rod unit combination model is as follows: Where x is the decision variable for the first stage. For the cost of second-stage inertia readjustment, This is due to the fan inertia deviation. Indicates normal operating constraints. Available inertia for partitioning, Minimum inertia requirement for partitioning.

10. A two-stage split-blown bar turbine unit combination system for a multi-regional power system considering the uncertainty of wind condition-related wind turbine inertia, characterized in that, include: The data input and storage module is used to input and store multi-regional power system operation data; The operational constraint modeling module is used to establish operating models for thermal power units, energy storage devices, system power balance, regional power balance, and regional tie-line power constraint models based on multi-regional power system operating data. The partitioned inertia modeling module is used to establish a partitioned inertia model, which represents the available inertia of each region in each time period as the sum of the synchronous machine inertia, wind turbine virtual inertia, energy storage virtual inertia and tie line support inertia, and determines the minimum inertia requirement of the corresponding region based on the maximum active power disturbance and the maximum allowable frequency change rate of the region. The wind turbine inertia uncertainty modeling module establishes a wind turbine inertia uncertainty model, obtains wind condition-related equivalent inertia estimates based on wind speed prediction data, and constructs a fuzzy set of wind turbine inertia prediction errors based on historical prediction error samples. The two-stage optimization module is used to establish a two-stage sparse brus unit combination model based on the multi-regional power system operation model, the zonal inertia model, and the wind turbine inertia uncertainty model. The first stage determines the day-ahead unit start-up and shutdown status, thermal power unit output, energy storage charging and discharging power, and regional tie line power based on the wind turbine inertia estimate. The second stage readjusts the synchronous machine inertia, energy storage inertia, and tie line support inertia after the wind turbine inertia prediction error is realized. The model transformation and solution module is used to transform the two-stage split-bulb unit combination model to obtain a mixed integer linear programming model and solve it. The results output module is used to output the unit combination results, power dispatch results, energy storage operation results, tie line transmission results, and zonal inertia safety verification results of the multi-regional power system.

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