Power transmission and distribution coordinated optimization control method applied to smart grid

Abstract

This invention relates to the field of power distribution control, addressing the problem that the additional heat accumulation risk under specific fluctuation phase combinations is systematically ignored, limiting the precision of transmission and distribution coordinated regulation. Specifically, it is a transmission and distribution coordinated optimization control method applied to smart grids. This method involves collecting the conductor temperature of transmission lines and the instantaneous power sequence at the grid connection point on the distribution side. The instantaneous power sequence is then frequency-domain transformed to extract the power fluctuation phase, and the thermal inertial response phase under the current thermal state is determined. The phase difference between the two is then calculated, allowing for dynamic adjustment of the line's response characteristics to load fluctuations based on the current line temperature. A comprehensive regulation intensity coefficient is obtained by scaling the temperature relative deviation rate and the phase difference against the reference value. This allows the regulation intensity to adaptively adjust with the temperature relative deviation rate and the phase difference, avoiding mismatch between the regulation intensity and the actual thermal state. This approach maximizes the transmission potential of the transmission channel while ensuring the safe operation of the line.

Description

Technical Field

[0001] This invention relates to the field of power distribution control, specifically to a power transmission and distribution coordinated optimization control method applied to smart grids. Background Technology

[0002] With the large-scale integration of new energy power generation, electric vehicle charging facilities, and various flexible loads, the operating characteristics of modern power systems have undergone profound changes. The load curve on the distribution network side has gradually evolved from the traditional unidirectional and steady-state type to a bidirectional and dynamic type, and the load fluctuation amplitude and frequency have increased significantly. This places higher demands on the safe operation and capacity management of transmission lines. In other words, how to fully tap the transmission potential of transmission channels while ensuring the safe operation of lines has become an important issue in the field of smart grid transmission and distribution coordinated optimization control.

[0003] In terms of transmission line current carrying capacity management, static thermal rating (STR) technology has long been a widely used conventional method. However, due to the real-time changes in meteorological conditions in the actual operating environment, static thermal rating often leads to a systematic underestimation of line capacity, failing to fully utilize the additional transmission capacity of the line under favorable environmental conditions. Therefore, dynamic thermal rating (DTR) technology has been proposed and gradually applied to engineering practice. It involves deploying online monitoring devices on transmission lines to collect parameters such as ambient temperature and wind speed in real time, and dynamically calculating the actual current carrying capacity of the line under the current environmental conditions based on the heat balance equation, thereby improving the utilization rate of transmission capacity while ensuring line safety.

[0004] However, the above technologies share a common blind spot: the power fluctuations of the distribution side load have specific frequency and phase characteristics, while the transmission line has thermal inertia, which causes the temperature change to lag behind the current change. When the frequency of the load fluctuation matches the thermal time constant of the line, the periodic current change will generate additional alternating thermal stress in the conductor, resulting in an equivalent heat generation higher than the calculated value based on the average current.

[0005] However, traditional DTR technology only focuses on meteorological parameters and the effective value of current, ignoring the frequency and phase information of load fluctuations. In particular, the thermal inertial response phase of transmission lines changes with temperature. Conventional technology only uses fixed thermal response parameters and cannot dynamically adjust its response characteristics to load fluctuations according to the current temperature of the line. This leads to the systematic neglect of the additional heat accumulation risk under specific fluctuation phase combinations, limiting the level of precision of transmission and distribution coordinated control.

[0006] To address the aforementioned technical problems, this invention proposes a solution. Summary of the Invention

[0007] This invention proposes a power transmission and distribution coordinated optimization control method for smart grids. This method involves collecting the conductor temperature of transmission lines and the instantaneous power sequence at the grid connection point on the distribution side. The instantaneous power sequence is then frequency-domain transformed to extract the power fluctuation phase, and the thermal inertial response phase under the current thermal state is determined. The phase difference between the two phases is calculated, allowing for dynamic adjustment of the line's response to load fluctuations based on the current line temperature. Using the relative temperature deviation rate as a reference value and scaling the reference value with the phase difference, a comprehensive control strength coefficient is obtained. This enables the control strength to adaptively adjust with the relative temperature deviation rate and the phase difference, avoiding mismatch between the control strength and the actual thermal state. Under the premise of ensuring the safe operation of the line, this method taps into the transmission potential of the transmission channel.

[0008] The objective of this invention can be achieved through the following technical solution: a power transmission and distribution coordinated optimization control method applied to smart grids, comprising the following steps:

[0009] Step 1: Collect the conductor temperature of the transmission line and the instantaneous power sequence of the grid connection point on the distribution side;

[0010] Step 2: Generate a relative temperature deviation rate based on the degree to which the conductor temperature deviates from the preset safe operating temperature limit; perform frequency domain transformation on the instantaneous power sequence, and extract the phase corresponding to the dominant frequency component of the power fluctuation in the instantaneous power sequence as the power fluctuation phase;

[0011] Step 3: Based on the relative temperature deviation rate, look up the pre-stored temperature-thermal inertial response phase correspondence table to determine the thermal inertial response phase of the transmission line under the current thermal state, and calculate the phase difference between the power fluctuation phase and the thermal inertial response phase.

[0012] Step 4: Using the relative temperature deviation rate as the reference value, scale the reference value by the phase difference, and use the scaled reference value as the comprehensive control strength coefficient; where the sign of the phase difference determines the scaling direction, the magnitude of the absolute value of the phase difference determines the scaling degree, the scaling degree increases with the increase of the absolute value, and the enhancement amplitude also increases with the increase of the absolute value.

[0013] Step 5: Generate phase control commands based on the comprehensive control strength coefficient, and control the power fluctuation phase to shift in the lagging direction. The shift amplitude is positively correlated with the comprehensive control strength coefficient.

[0014] In a preferred embodiment of the present invention, the calculation of the relative temperature deviation rate is specifically as follows:

[0015] Calculate the difference between the conductor temperature and the preset minimum operating temperature to obtain the conductor difference; then calculate the difference between the preset upper limit of safe operating temperature and the minimum operating temperature to obtain the design difference.

[0016] Calculate the conductor difference and the design difference, and use the calculated ratio as the relative temperature deviation rate.

[0017] In a preferred embodiment of the present invention, the method for obtaining the power fluctuation phase in step two is as follows:

[0018] Using a preset time length as a sliding time window, a sliding window frequency domain analysis is performed on the instantaneous power sequence to obtain each frequency component and its corresponding amplitude and phase;

[0019] The amplitude corresponding to each frequency component is compared with a preset threshold, and all frequency components whose amplitude exceeds the preset threshold are selected as candidate frequency components.

[0020] When there are multiple candidate frequency components, the phase corresponding to each candidate frequency component is extracted, and the weighted average of each phase is used as the power fluctuation phase output. The weight of each candidate frequency component is determined according to the matching degree between the amplitude of the candidate frequency component and the thermal time constant of the transmission line.

[0021] When no frequency component has an amplitude exceeding the preset threshold, the frequency component with the largest amplitude is selected as the candidate frequency component.

[0022] When there is only one candidate frequency component, the phase corresponding to the candidate frequency component is used as the power fluctuation phase output.

[0023] In a preferred embodiment of the present invention, the method for obtaining the temperature-thermal inertia response phase correspondence table in step three is as follows:

[0024] The transmission line is placed in multiple preset temperature environments. Test power fluctuations are applied to the transmission line respectively, and the phase lag of the transmission line to the test power fluctuations under each temperature environment is measured. The relative deviation rate of each temperature and its corresponding phase lag are stored in a correspondence table in the form of data pairs. The thermal inertial response phase between adjacent calibration points in the correspondence table is determined by linear interpolation.

[0025] In a preferred embodiment of the present invention, the step of scaling the reference value in step four is as follows:

[0026] The phase difference is input into a preset mapping function, where the mapping function maps the phase difference to a scaling factor, and the scaling factor is used as the scaling degree.

[0027] The mapping function has different mapping slopes in the positive and negative intervals of the phase difference, with the mapping slope in the positive interval being greater than that in the negative interval. This makes the mapping function more sensitive to changes in phase difference in the positive interval than in the negative interval, and causes the scaling factor to increase non-linearly with the absolute value of the phase difference in the positive interval and decrease with the absolute value of the phase difference in the negative interval.

[0028] In a preferred embodiment of the present invention, the output value of the mapping function increases exponentially with the increase of the absolute value of the input in the positive range of the phase difference, and the exponent of the exponent of the power function is greater than 1.

[0029] In the negative range of the phase difference data, the absolute value of the output value of the mapping function decreases linearly as the absolute value of the input increases, and the minimum value of the mapping slope in the positive range is greater than the maximum value of the mapping slope in the negative range.

[0030] In a preferred embodiment of the present invention, the method for obtaining the comprehensive control strength coefficient based on the benchmark value in step four is as follows:

[0031] The power operation result is obtained by exponentiation with the absolute value of the phase difference data as the exponent and the sum of the relative temperature deviation rate and 1 as the base.

[0032] If the phase difference data is positive, the result of the exponentiation is used as the comprehensive control strength coefficient; if the phase difference data is negative, the reciprocal of the result of the exponentiation is used as the comprehensive control strength coefficient.

[0033] In a preferred embodiment of the present invention, the method for generating the phase modulation command in step six is ​​as follows:

[0034] Obtain the preset upper limit value of phase offset, calculate the product of the comprehensive control strength coefficient and the upper limit value of phase offset, and record it as the target offset. The target offset has a directional attribute, and the direction is the lag direction. Then, send the phase control command containing the target offset to the distribution side equipment.

[0035] The power distribution equipment superimposes a phase offset equal to the target offset into the voltage phase of the output power.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. In this invention, by collecting the conductor temperature of the transmission line and the instantaneous power sequence of the grid connection point on the distribution side, the instantaneous power sequence is frequency domain transformed to extract the power fluctuation phase. The temperature-thermal inertial response phase correspondence table is consulted to determine the thermal inertial response phase of the transmission line under the current thermal state. Then, the phase difference between the power fluctuation phase and the thermal inertial response phase is calculated. This overcomes the shortcomings of the prior art, which uses fixed thermal response parameters and cannot dynamically adjust its response characteristics to load fluctuations according to the current temperature of the line. This enables the transmission and distribution coordinated control to simultaneously take into account the frequency characteristics and phase characteristics of load fluctuations and the current thermal state of the line, reducing the possibility that the additional heat accumulation risk under a specific combination of fluctuation phases is systematically ignored.

[0038] 2. This invention uses the relative temperature deviation rate as a reference value, scales the reference value using the phase difference, and uses the scaled reference value as the comprehensive control strength coefficient. The sign of the phase difference determines the scaling direction, the magnitude of the absolute value of the phase difference determines the scaling degree, and the enhancement amplitude increases with the increase of the absolute value. This allows the comprehensive control strength coefficient to adaptively adjust with changes in the relative temperature deviation rate and the phase difference, avoiding the shortcomings of mismatch between the control strength and the actual thermal state when using fixed thermal response parameters, and improving the precision level of power transmission and distribution coordinated optimization control.

[0039] 3. The present invention also generates phase control commands based on the comprehensive control strength coefficient, controls the power fluctuation phase to shift in the lagging direction, and the shift amplitude is positively correlated with the comprehensive control strength coefficient, so that the power fluctuation phase at the grid connection point on the distribution side is actively adjusted in a direction that is coordinated with the thermal inertia response phase of the transmission line, thereby tapping the transmission potential of the transmission channel under the premise of ensuring the safe operation of the line, effectively utilizing the extra transmission capacity of the line under favorable environmental conditions, and improving the transmission capacity utilization rate. Attached Figure Description

[0040] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 This is a system block diagram of the present invention;

[0042] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1: Please refer to Figure 1 - Figure 2 As shown, the power transmission and distribution coordinated optimization control method applied to smart grids includes the following steps:

[0045] Step 1: Collect the conductor temperature of the transmission line and the instantaneous power sequence of the grid connection point on the distribution side;

[0046] The conductor temperature is measured by an online temperature monitoring device deployed on the conductor of the transmission line. Since the radial dimension of the transmission line is small and the thermal conductivity is high, the difference between the surface temperature and the core temperature can be ignored under steady-state and quasi-steady-state conditions. The surface temperature can characterize the overall thermal state of the conductor. Therefore, the surface temperature of the transmission line is used as the conductor temperature.

[0047] Regarding the instantaneous power sequence, the instantaneous power sequence is the sequence of instantaneous three-phase active power values ​​at the grid connection point on the distribution side. The unit of instantaneous power is kilowatt or megawatt, and its value is obtained by summing the product of the instantaneous voltage value and the instantaneous current value at the grid connection point on the distribution side.

[0048] Step 2: Based on the degree to which the conductor temperature deviates from the preset upper limit of safe operating temperature, generate the relative temperature deviation rate, perform frequency domain transformation on the instantaneous power sequence, and extract the phase corresponding to the dominant frequency component of the power fluctuation in the instantaneous power sequence as the power fluctuation phase;

[0049] The preset upper limit of safe operating temperature is determined according to the conductor material, insulation type and operating procedures of the transmission line. For conventional steel-cored aluminum stranded wire, the upper limit of safe operating temperature is 70℃ to 80℃. For heat-resistant aluminum alloy conductors, the upper limit of safe operating temperature is 150℃ to 200℃. For composite material core conductors, the upper limit of safe operating temperature is 160℃ to 240℃.

[0050] The minimum operating temperature is determined by taking the higher of the two values: the historical extreme minimum ambient temperature of the area where the line is located and the steady-state temperature of the conductor when it is not energized or lightly loaded at the minimum ambient temperature.

[0051] The calculation of the relative temperature deviation rate is as follows:

[0052] Calculate the difference between the conductor temperature and the preset minimum operating temperature to obtain the conductor difference; then calculate the difference between the preset upper limit of safe operating temperature and the minimum operating temperature to obtain the design difference.

[0053] The ratio between the conductor difference and the design difference is calculated, and the calculated ratio is used as the temperature relative deviation rate. The temperature relative deviation rate is dimensionless and its value ranges from 0 to 1. The larger the value, the closer the conductor temperature is to the upper limit of the safe operating temperature, and the higher the risk of heat accumulation.

[0054] The method for obtaining the phase of power fluctuation is as follows:

[0055] Using a preset time window as the sliding time window, the instantaneous power sequence is subjected to sliding window frequency domain analysis to obtain each frequency component and its corresponding amplitude and phase. The specific algorithm for frequency domain transformation adopts Fast Fourier Transform (FFT). The preset time window is 10 minutes for example, and the window sliding step is 1 minute for example. Before performing FFT, the instantaneous power sequence within the sliding time window is subjected to mean removal and Hanning window processing to suppress spectral leakage. The output of FFT is the complex form of each frequency component, with its magnitude as the amplitude and its phase angle as the phase. The frequency resolution is determined by the preset time window. For example, when the preset time window is 10 minutes, the frequency resolution is 1 / 600 second ≈ 0.00167 Hz.

[0056] The amplitude of each frequency component is compared with a preset threshold. All frequency components whose amplitude exceeds the preset threshold are selected as candidate frequency components. The preset threshold is determined based on the average power of the instantaneous power sequence and the rated capacity of the line. Specifically, the preset threshold can be 5% to 10% of the average instantaneous power within the sliding time window, or 1% to 5% of the rated capacity of the transmission line, whichever is smaller. The principle for setting the preset threshold is to filter out measurement noise and small fluctuations in normal load, while retaining power fluctuation components that may have a significant impact on the thermal state of the conductor.

[0057] When there are multiple candidate frequency components, the phase corresponding to each candidate frequency component is extracted, and the weighted average of each phase is used as the power fluctuation phase output. The weight of each candidate frequency component is determined based on the matching degree between the amplitude of the candidate frequency component and the thermal time constant of the transmission line. The thermal time constant is obtained as follows:

[0058] For transmission lines with known conductor type and line structure, the thermal time constant can be calculated based on the thermal parameters (including conductor mass, specific heat capacity, heat dissipation area and heat dissipation coefficient) provided by the conductor manufacturer, and is an inherent parameter of the line.

[0059] For transmission lines in operation, the thermal time constant can also be obtained through online identification. Specifically, a known power step disturbance is applied to the transmission line, the response curve of the conductor temperature is recorded, and the thermal time constant is obtained by fitting the response curve with time constant. The unit of thermal time constant is seconds, and its physical meaning is the time required for the conductor temperature response power change to reach 63.2% of the steady-state value.

[0060] The specific method for determining the weight of the candidate frequency component is as follows: For the i-th candidate frequency component, its frequency is fi and the corresponding period is Ti = 1 / fi. Calculate the matching degree between the period Ti of the candidate frequency component and the thermal time constant τ of the transmission line.

[0061] The matching degree can be represented by the matching degree function Mi. Mi = 1 / (1 + |log(Ti / τ)|), where ln is the natural logarithm. When Ti = τ, Mi = 1, indicating that the period of the candidate frequency component is perfectly matched with the thermal time constant, and the alternating thermal stress generated by the power fluctuation in the conductor is the maximum. When Ti deviates from τ, Mi gradually decreases, indicating that the matching degree decreases.

[0062] The weight wi of each candidate frequency component is obtained by normalizing the matching degree Mi, i.e., wi=Mi / ΣMj, where ΣMj is the sum of the matching degrees of all candidate frequency components, and the power fluctuation phase φp=Σ(wi×φi), where φi is the phase of the i-th candidate frequency component.

[0063] When no frequency component has an amplitude exceeding the preset threshold, the frequency component with the largest amplitude is selected as the candidate frequency component.

[0064] When there is only one candidate frequency component, the phase corresponding to the candidate frequency component is used as the power fluctuation phase output.

[0065] Step 3: Based on the relative temperature deviation rate, look up the pre-stored temperature-thermal inertial response phase correspondence table to determine the thermal inertial response phase of the transmission line under the current thermal state, and calculate the phase difference between the power fluctuation phase and the thermal inertial response phase. φp is the power fluctuation phase, and φth is the thermal inertial response phase. When the phase difference is positive, it indicates that the power fluctuation phase leads the thermal inertial response phase; when the phase difference is negative, it indicates that the power fluctuation phase lags behind the thermal inertial response phase.

[0066] The pre-calibration method for the temperature-thermal inertial response phase correspondence table is as follows:

[0067] The transmission line is placed in multiple preset temperature environments. Test power fluctuations are applied to the transmission line respectively, and the phase lag of the transmission line to the test power fluctuations under each temperature environment is measured. The relative deviation rate of each temperature and its corresponding phase lag are stored in a correspondence table in the form of data pairs. The thermal inertial response phase between adjacent calibration points in the correspondence table is determined by linear interpolation.

[0068] Step 4: Using the relative temperature deviation rate as the reference value, scale the reference value by the phase difference. Use the scaled reference value as the comprehensive control strength coefficient. The sign of the phase difference determines the scaling direction, and the magnitude of the absolute value of the phase difference determines the scaling degree. The scaling degree increases with the increase of the absolute value, and the enhancement amplitude also increases with the increase of the absolute value. The steps for scaling the reference value are as follows:

[0069] Input the phase difference into a preset mapping function, which maps the phase difference into a scaling factor, and uses the scaling factor as the scaling degree.

[0070] The mapping function has different mapping slopes in the positive and negative intervals of the phase difference, and the mapping slope in the positive interval is greater than that in the negative interval. This makes the mapping function more sensitive to changes in phase difference in the positive interval than in the negative interval. It also makes the scaling factor increase non-linearly with the absolute value of the phase difference in the positive interval and decrease with the absolute value of the phase difference in the negative interval.

[0071] The mapping function is expressed as follows in the positive range of the phase difference: , where a is the proportionality coefficient, n is the power exponent and n>1, |Δφ| is the absolute value of the phase difference, and its output value increases with the increase of the absolute value of the input in a power function, and the exponent of the power function is greater than 1;

[0072] In the negative range of the phase difference data, the mapping function is expressed as follows: Where b is the proportional coefficient, |Δφ| is the absolute value of the phase difference, the absolute value of the output value decreases linearly as the absolute value of the input increases, and the minimum value of the mapping slope in the positive range is greater than the maximum value of the mapping slope in the negative range.

[0073] The method for obtaining the comprehensive regulatory intensity coefficient from the benchmark value is as follows:

[0074] The power operation result is obtained by exponentiation with the absolute value of the phase difference data as the exponent and the sum of the relative temperature deviation rate and 1 as the base.

[0075] If the phase difference data is positive, the result of the exponentiation is used as the comprehensive control strength coefficient; if the phase difference data is negative, the reciprocal of the exponentiation result is used as the comprehensive control strength coefficient. Specifically, the final calculation formula for the comprehensive control strength coefficient is as follows:

[0076] When Δφ is a positive value ;

[0077] When Δφ is negative ;

[0078] Where λ is the comprehensive control strength coefficient, η is the relative temperature deviation rate, Δφ is the phase difference, and φref is the preset phase reference value, which is used to normalize the absolute value of the phase difference. The unit of φref is consistent with the unit of the phase difference.

[0079] Step 5: Generate phase control commands based on the comprehensive control strength coefficient to control the phase of power fluctuations to shift in the lagging direction. The shift amplitude is positively correlated with the comprehensive control strength coefficient.

[0080] The method for generating phase modulation commands is as follows:

[0081] Obtain the preset upper limit value of phase offset, calculate the product of the comprehensive control strength coefficient and the upper limit value of phase offset, and record it as the target offset. The target offset has a directional attribute, and the direction is the lag direction. Then, send the phase control command containing the target offset to the distribution side equipment. The distribution side equipment can be any one of the following: grid-connected inverter with phase adjustment capability, flexible AC transmission device, or static var compensator.

[0082] The distribution-side equipment adds a phase offset equal to the target offset to the voltage phase of the output power.

[0083] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A power transmission and distribution collaborative optimization control method applied to a smart grid, characterized in that, Includes the following steps: Step 1: Collect the conductor temperature of the transmission line and the instantaneous power sequence of the grid connection point on the distribution side; Step 2: Generate a relative temperature deviation rate based on the degree to which the conductor temperature deviates from the preset safe operating temperature limit; perform frequency domain transformation on the instantaneous power sequence, and extract the phase corresponding to the dominant frequency component of the power fluctuation in the instantaneous power sequence as the power fluctuation phase; Step 3: Based on the relative temperature deviation rate, look up the pre-stored temperature-thermal inertial response phase correspondence table to determine the thermal inertial response phase of the transmission line under the current thermal state, and calculate the phase difference between the power fluctuation phase and the thermal inertial response phase. Step 4: Using the relative temperature deviation rate as the reference value, scale the reference value by the phase difference, and use the scaled reference value as the comprehensive control strength coefficient. The sign of the phase difference determines the scaling direction, and the magnitude of the absolute value of the phase difference determines the scaling degree. The scaling degree increases with the increase of the absolute value, and the enhancement magnitude also increases with the increase of the absolute value. Step 5: Generate phase control commands based on the comprehensive control strength coefficient, and control the power fluctuation phase to shift in the lagging direction. The shift amplitude is positively correlated with the comprehensive control strength coefficient.

2. The transmission and distribution collaborative optimization control method for smart grid according to claim 1, characterized in that, The calculation of the relative temperature deviation rate is as follows: Calculate the difference between the conductor temperature and the preset minimum operating temperature to obtain the conductor difference value; Next, calculate the difference between the preset upper limit of safe operating temperature and the minimum operating temperature to obtain the design difference. Calculate the ratio between the conductor difference and the design difference, and use the calculated ratio as the relative temperature deviation rate.

3. The power transmission and distribution coordinated optimization control method for smart grids according to claim 1, characterized in that, The method for obtaining the power fluctuation phase in step two is as follows: Using a preset time length as a sliding time window, a sliding window frequency domain analysis is performed on the instantaneous power sequence to obtain each frequency component and its corresponding amplitude and phase; The amplitude corresponding to each frequency component is compared with a preset threshold, and all frequency components whose amplitude exceeds the preset threshold are selected as candidate frequency components. When there are multiple candidate frequency components, the phase corresponding to each candidate frequency component is extracted, and the weighted average of each phase is used as the power fluctuation phase output. The weight of each candidate frequency component is determined according to the matching degree between the amplitude of the candidate frequency component and the thermal time constant of the transmission line. When no frequency component has an amplitude exceeding the preset threshold, the frequency component with the largest amplitude is selected as the candidate frequency component. When there is only one candidate frequency component, the phase corresponding to the candidate frequency component is used as the power fluctuation phase output.

4. The power transmission and distribution coordinated optimization control method for smart grids according to claim 1, characterized in that, The method for obtaining the temperature-thermal inertia response phase correspondence table in step three is as follows: The transmission line is placed in multiple preset temperature environments. Test power fluctuations are applied to the transmission line respectively, and the phase lag of the transmission line to the test power fluctuations under each temperature environment is measured. The relative deviation rate of each temperature and its corresponding phase lag are stored in a correspondence table in the form of data pairs. The thermal inertial response phase between adjacent calibration points in the correspondence table is determined by linear interpolation.

5. The power transmission and distribution coordinated optimization control method for smart grids according to claim 1, characterized in that, The scaling steps for the reference value in step four are as follows: The phase difference is input into a preset mapping function, where the mapping function maps the phase difference to a scaling factor, and the scaling factor is used as the scaling degree. The mapping function has different mapping slopes in the positive and negative intervals of the phase difference, with the mapping slope in the positive interval being greater than that in the negative interval. This makes the mapping function more sensitive to changes in phase difference in the positive interval than in the negative interval, and causes the scaling factor to increase non-linearly with the absolute value of the phase difference in the positive interval and decrease with the absolute value of the phase difference in the negative interval.

6. The power transmission and distribution coordinated optimization control method for smart grids according to claim 5, characterized in that, In the positive range of phase difference, the output value of the mapping function increases exponentially with the increase of the absolute value of the input, and the exponent of the power function is greater than 1. In the negative range of the phase difference data, the absolute value of the output value of the mapping function decreases linearly as the absolute value of the input increases, and the minimum value of the mapping slope in the positive range is greater than the maximum value of the mapping slope in the negative range.

7. The power transmission and distribution coordinated optimization control method for smart grids according to claim 1, characterized in that, The method for obtaining the comprehensive control strength coefficient based on the benchmark value in step four is as follows: The power operation result is obtained by exponentiation with the absolute value of the phase difference data as the exponent and the sum of the relative temperature deviation rate and 1 as the base. The sign of the phase difference data is used to determine the overall control strength coefficient: if the phase difference data is positive, the result of the exponentiation is used as the comprehensive control strength coefficient. If the phase difference data is negative, the reciprocal of the result of the exponentiation is used as the comprehensive control strength coefficient.

8. The power transmission and distribution coordinated optimization control method for smart grids according to claim 1, characterized in that, The method for generating the phase modulation command in step six is ​​as follows: Obtain the preset upper limit value of phase offset, calculate the product of the comprehensive control strength coefficient and the upper limit value of phase offset, and record it as the target offset. The target offset has a directional attribute, and the direction is the lag direction. Then, send the phase control command containing the target offset to the distribution side equipment. The power distribution equipment superimposes a phase offset equal to the target offset into the voltage phase of the output power.

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