Metro power quality collaborative rolling optimization fast treatment method and system containing distributed energy

Abstract

The application discloses a metro electric energy quality collaborative rolling optimization rapid treatment method and system containing distributed energy, and the method firstly acquires train operation real-time data and predicts traction power and harmonics; then, a sensitivity matrix representing the influence relationship of each station and the distributed energy on the electric energy quality of the 110kV side of the power grid is constructed; finally, based on a composite mechanism of 'period triggering + event triggering', multi-objective rolling optimization solving is carried out, and reactive power and harmonic compensation instructions of SVG, APF and distributed energy inverters of each station are dynamically coordinated and dispatched. Through inter-station collaboration and multi-device rapid response, the application solves the electric energy quality problems such as voltage deviation, harmonic overproof and flicker caused by frequent start and stop of trains and power fluctuation of the distributed energy.

Description

Technical Field

[0001] This invention belongs to the field of power quality management in rail transit, specifically relating to a rapid management method and system for collaborative rolling optimization of power quality in subways with distributed energy sources. Background Technology

[0002] With the continuous expansion of urban rail transit, power quality issues in subway power supply systems are becoming increasingly prominent. Subway systems contain numerous nonlinear loads, such as rectifiers and inverters, which introduce significant harmonics, voltage fluctuations, and flicker during train startup, acceleration, and braking, severely impacting power grid quality. Furthermore, while the integration of distributed energy sources into rail transit systems improves energy efficiency, the randomness and volatility of their output further complicate power quality management. Therefore, achieving rapid and accurate power quality management in subway systems within a multi-source collaborative framework has become a critical issue that urgently needs to be addressed in rail transit power supply systems.

[0003] Existing research on subway power quality management primarily employs static compensation or local optimization strategies, such as fixed-capacity reactive power compensation devices or active filters, lacking dynamic coordination mechanisms among multiple stations and devices. Traditional methods struggle to adapt to the drastic power fluctuations during train operation, exhibiting slow response times and limited effectiveness. Furthermore, most existing optimization models are based on single objectives or offline calculations, failing to fully consider the multi-objective coordination and real-time optimization requirements after distributed energy integration, making it difficult to balance efficiency and economy in practical operation. Therefore, a dynamic power quality management method capable of achieving inter-station coordination and possessing rapid response capabilities is urgently needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a rapid governance method and system for collaborative rolling optimization of subway power quality, including distributed energy sources. This method solves power quality problems such as voltage deviation, harmonic exceedance, and flicker caused by frequent train starts and stops and power fluctuations of distributed energy sources, significantly improving the impact of the subway power supply system on the power grid's power quality.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A rapid governance method for collaborative rolling optimization of subway power quality with distributed energy resources includes:

[0007] Step S1: Obtain key data on actual subway operation;

[0008] Step S2: Based on key data from actual operation, predict traction power, AC measured power, and harmonics;

[0009] Step S3: Based on the predicted traction power, measured AC power, and harmonics, the fundamental voltage deviation and total harmonic distortion are determined by using the sensitivity of the relationship between each station and distributed energy source on the power quality of the 110kV side of the power grid.

[0010] Step S4: Based on the fundamental voltage deviation and total harmonic distortion, construct the decision quantity, objective function and constraints; perform multi-objective rolling optimization to obtain the reactive power and harmonic compensation commands for the dynamic reactive power compensation devices, active filtering devices and distributed energy inverters of each site for dynamic coordination and scheduling.

[0011] Furthermore, the key data for actual subway operation mentioned in step S1 includes train timetables, real-time train positions and speeds (v). m [k] DC side rated voltage U dc AC side rated line voltage U LL Rated voltage U at grid connection point n The capacity of the dynamic reactive power compensation device for the AC busbars of each traction substation With the slope rate r Q Rated harmonic current compensation capacity of active filter device Reactance and equivalent resistance parameters, measurement and communication cycle Δt and rolling time domain T, and available active power prediction and reactive power curve of grid-connected distributed energy units.

[0012] Furthermore, the predicted traction power is:

[0013] ;

[0014] in, Let m be the traction power of the m-th train at time k. m Let a be the mass of the m-th train. m [k] represents the acceleration of the m-th train at time k, v m [k] represents the speed of the m-th train at time k, θ m [k] represents the gradient angle at time k where the m-th train is located, and c r Where ρ is the rolling resistance coefficient, ρ is the air density, and C is the rolling resistance coefficient. d Where A is the drag coefficient, g is the frontal area, and η is the acceleration due to gravity. m [k] represents the traction efficiency;

[0015] All power of trains on the network By allocating the DC resistance network to the i-th traction substation, we obtain:

[0016] ;

[0017] ;

[0018] ;

[0019] Among them, P dc,i [k] and I dc,i [k] represents the power and current on the DC side of station i, respectively, and β i,m [k] is the power sharing coefficient from train m to station i, U dc R is the rated voltage on the DC side. i,m [k] represents the equivalent resistance of the corresponding circuit.

[0020] Furthermore, the prediction of AC power and harmonics includes:

[0021] Predicted AC active power is:

[0022] ;

[0023] Among them, P i [k] represents the active power of station i on the AC side, η tr,i For transformer efficiency, η rec,i For rectifier efficiency;

[0024] Predicted AC reactive power is:

[0025] ;

[0026] ;

[0027] ;

[0028] Where, μ i [k] is the commutation overlap angle, ω is the angular frequency, and L eq,i For the equivalent commutation reactance, U Φ,i PF is the effective value of the phase voltage. i [k] represents the power factor, THD. I,i [k] represents the total harmonic distortion of the AC side current, Q i [k] represents the reactive power on the AC side of station i;

[0029] The harmonics are:

[0030] ;

[0031] Among them, I h,i [k] represents the intensity of the h-th harmonic current, k h,i [k] is the h-th proportionality coefficient related to the load rate, and the set is the set of 24 pulse characteristics and residual harmonics.

[0032] Furthermore, the fundamental voltage deviation is:

[0033] ;

[0034] Where ΔU[k] is the bus voltage deviation at the 110kV grid connection point, S UQ,i The sensitivity of reactive power to voltage at station i. For the net reactive power injection of station i, S UIh,i,h To determine the sensitivity of the h-th harmonic current of station i to voltage, S UP,l For the active power sensitivity to voltage of the l-th distributed energy unit, P l [k] represents the active power output of the l-th distributed energy unit;

[0035] The total harmonic distortion is:

[0036] ;

[0037] ;

[0038] Among them, U h [k] represents the h-th harmonic voltage, H i,h Injecting sensitivity to the h-th harmonic voltage into station i. H is the net harmonic current of station i. l,h The sensitivity of the distributed energy unit to the h-th harmonic voltage. U1[k] is the reference for the h-th harmonic current of the distributed energy inverter, and U1[k] is the fundamental voltage.

[0039] Furthermore, the construction of decision variables, objective functions, and constraints includes:

[0040] The decision-making quantity is:

[0041] ;

[0042] Where u[k] is the control sequence from time k to k+N-1. For SVG, no power consumption. P is the reference for the h-th harmonic of the APF. l and Q l These are distributed energy resources with and without reactive power. For the harmonic reference of distributed energy, N is the number of rolling steps, T is the rolling time domain, and Δt is the sampling step size;

[0043] The objective function is constructed as follows:

[0044] ;

[0045] Where J is the optimization objective, P st [j] represents short-time flicker, THD[j] represents total harmonic distortion, ΔU[j] represents voltage deviation, w1~w6 represent target weights, and c Q,i and c I,i,hThese are the reactive power / harmonic power output cost coefficients for station i, and c. curt c is the cost coefficient for power curtailment. cycle Let (x) be the equivalent cost coefficient for the energy storage cycle. + =max(x,0), P av,l The short-time flicker P is available as active power for distributed energy; st [j] represents the instantaneous flicker perception P of the flicker meter output within the window. inst (t) Perform quantile statistics and take P 0.1 P1, P3, P 10 P 50 Equal quantile values ​​were then weighted according to standard parameters.

[0046] The constraints are as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] in, Rated reactive power for SVG, r Q For SVG ramp rate, The rated current for the h-th harmonic of the APF. The PQ capability curve for distributed energy inverters. This refers to the harmonic rated current for distributed energy resources.

[0054] Furthermore, a combined mechanism of periodic triggering and event triggering is employed to solve multi-objective rolling optimization problems; wherein:

[0055] The periodic triggering mechanism performs a multi-objective rolling optimization solution once every Δt.

[0056] The event triggering mechanism is as follows: when events such as train entering / leaving the station, grid connection point voltage deviation or THD threshold exceeding, SVG / APF state switching, or sudden change in distributed energy output occur, an additional multi-objective rolling optimization solution is immediately added. The event triggering and window / weight reconfiguration are as follows:

[0057] ;

[0058] ;

[0059] in, This is an event trigger indicator; it is 1 if the condition is met, and 0 otherwise. The measured voltage deviation at the grid connection point. , These are the event thresholds for voltage deviation and THD, respectively. For detecting train arrival / departure station operation events. , , These represent the temporary window length, smoothing weight, and time weight that take effect after the event is triggered, respectively. , , This is the event scaling factor. Adaptive weights for subway operating conditions.

[0060] Furthermore, the adaptive weights for subway operating conditions for:

[0061] ;

[0062] in, Based on time weighting, This is the weighted sensitivity coefficient. The train density is based on time k. for The moving average over a period of time is used as the normalization benchmark.

[0063] Furthermore, when performing multi-objective rolling optimization, the rolling window objective value is obtained by windowing the objective, performing time summation and motion smoothing:

[0064]

[0065] in, Let k be the target value for the rolling window starting at time k. The time weight of step j within the window. For the stage goal of step j, Let j be the state vector at step j. Let γ be the control vector at step j, and let γ be the action smoothing weight, used to suppress frequent or excessive control jumps.

[0066] A rapid governance system for collaborative rolling optimization of subway power quality, incorporating distributed energy resources, includes:

[0067] The data acquisition unit is used to acquire key data on the actual operation of the subway.

[0068] The prediction unit predicts traction power, AC measured power, and harmonics based on key data from actual operation.

[0069] The fundamental voltage deviation and total harmonic distortion calculation unit, based on the predicted traction power and AC measured power and harmonics, uses the sensitivity characterizing the relationship between each station and distributed energy source on the power quality of the 110kV side of the power grid to determine the fundamental voltage deviation and total harmonic distortion.

[0070] The multi-objective rolling optimization solution unit constructs decision quantities, objective functions, and constraints based on fundamental voltage deviation and total harmonic distortion. Based on a composite mechanism of periodic triggering and event triggering, it performs multi-objective rolling optimization to obtain reactive power and harmonic compensation commands for the dynamic reactive power compensation devices, active filtering devices, and distributed energy inverters of each site for dynamic coordination and scheduling.

[0071] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0072] (1) Fast response and stable convergence: The fast response mechanism triggered by "cycle + event" is adopted. When encountering sudden fluctuations such as train arrival and departure, regenerative braking, etc., the window will be shortened and the weight will be switched automatically. It can bring voltage deviation, THD and flicker back to the limit in seconds and suppress control jitter, significantly reducing the peak value and duration of exceeding the limit.

[0073] (2) Inter-station-intra-station multi-entity collaborative governance: Through multi-site collaboration, cross-site coupling is brought under control. Within the station, SVG and APF coordinate their actions according to the sensitivity sharing principle and priority rules to avoid mutual interference and chain oscillations, thereby improving system-level power quality and governance costs simultaneously. Attached Figure Description

[0074] Figure 1 This is a flowchart of a rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources, according to the present invention.

[0075] Figure 2 This is a schematic diagram of the city's power grid and subway power supply network. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0077] This invention proposes a rapid governance method for metro power quality incorporating distributed energy resources through collaborative rolling optimization. First, real-time train operation data is acquired and traction power and harmonics are predicted. Then, a sensitivity matrix characterizing the impact of each station and distributed energy resources on the 110kV side of the power grid is constructed, and fundamental voltage deviation, harmonic voltage, and THD are calculated. Finally, based on a composite mechanism of "periodic triggering + event triggering," multi-objective rolling optimization is performed to dynamically coordinate and schedule the SVG, APF, and reactive power and harmonic compensation commands of the distributed energy inverters at each station. Figure 1 As shown, the method specifically includes the following steps:

[0078] S1. Obtain key data from actual operation;

[0079] A schematic diagram of the city power grid and subway power supply network is shown below. Figure 2 As shown, obtain the train timetable, real-time train position and speed v. m [k] DC side rated voltage U dc AC side rated line voltage U LL Rated voltage U at grid connection point n The capacity of the dynamic var compensator (SVG) in each traction substation With the slope rate r Q Rated harmonic current compensation capacity of active power filter (APF) Reactance and equivalent resistance parameters, measurement and communication cycle Δt and rolling time domain T, and available active power prediction and reactive power curve of grid-connected distributed energy units.

[0080] S2. Based on key operational data, predict traction power, AC measured power, and harmonics; including:

[0081] The traction power of the m-th train at time k for:

[0082]

[0083] Where, m m Let a be the mass of the m-th train. m [k] represents the acceleration of the m-th train at time k, v m [k] represents the speed of the m-th train at time k, θ m [k] represents the gradient angle at time k where the m-th train is located, and c r Where ρ is the rolling resistance coefficient, ρ is the air density, and C is the rolling resistance coefficient. d Where A is the drag coefficient, g is the frontal area, and η is the acceleration due to gravity. m [k] represents the traction efficiency.

[0084] All power of trains in the network should be distributed to the i-th traction substation according to the DC resistance network:

[0085]

[0086]

[0087]

[0088] Among them, P dc,i [k] and I dc,i [k] represents the power and current on the DC side of station i, respectively, and β i,m [k] is the power sharing coefficient from train m to station i, U dc R is the rated voltage on the DC side. i,m [k] represents the equivalent resistance of the corresponding circuit.

[0089] Predicted AC measurement parameters:

[0090] The active power and commutation overlap angle are:

[0091]

[0092]

[0093] Among them, P i [k] represents the active power of station i on the AC side, η tr,i For transformer efficiency, η rec,i For rectifier efficiency, μ i [k] is the commutation overlap angle, ω is the angular frequency, and L eq,i For the equivalent commutation reactance, U Φ,i This is the effective value of the phase voltage.

[0094] The power factor and reactive power are:

[0095]

[0096]

[0097] Among them, PF i [k] represents the power factor, THD. I,i [k] represents the total harmonic distortion of the AC side current, Q i [k] represents the reactive power of station i on the AC side.

[0098] The harmonics are:

[0099]

[0100] Among them, I h,i [k] represents the intensity of the h-th harmonic current, k h,i[k] is the h-th proportionality coefficient related to the load rate, and the set is the set of 24 pulse characteristics and residual harmonics.

[0101] S3. Construct the inter-station coupling sensitivity matrix and calculate the fundamental voltage deviation, harmonic voltage, and THD; specifically including:

[0102] Construct the fundamental voltage deviation sensitivity matrix and calculate the fundamental voltage deviation:

[0103]

[0104]

[0105] Where ΔU[k] is the bus voltage deviation at the 110kV grid connection point, S UQ,i The sensitivity of reactive power to voltage at station i. For the net reactive power injection of station i, S UIh,i,h To determine the sensitivity of the h-th harmonic current of station i to voltage, S UP,l For the active power sensitivity to voltage of the l-th distributed energy unit, P l [k] represents the active power output of the l-th distributed energy unit, H ΔU The voltage deviation sensitivity matrix is ​​derived from the reactive power sensitivity S of station i to voltage. UQ,i The sensitivity of the h-th harmonic current to voltage at station i is S. UIh,i,h The sensitivity of the active power to voltage of the l-th distributed energy unit. UP,l The structure consists of n and m, which are the last station and the distributed energy number, respectively.

[0106] The harmonic voltage and its sensitivity matrix are as follows:

[0107]

[0108]

[0109] THD was also calculated:

[0110]

[0111] Among them, U h [k] represents the h-th harmonic voltage, THD[k] represents the voltage harmonic distortion rate, and H Uh H is the voltage deviation sensitivity matrix. i,h Injecting sensitivity to the h-th harmonic voltage into station i. Let N be the net harmonic current of station i. l,h The sensitivity of the distributed energy unit to the h-th harmonic voltage. U1[k] is the reference for the h-th harmonic current of the distributed energy inverter, and U1[k] is the fundamental voltage.

[0112] S4. Based on fundamental voltage deviation and total harmonic distortion, construct decision quantities, objective functions, and constraints; based on a composite mechanism of periodic triggering and event triggering, perform multi-objective rolling optimization to obtain reactive power and harmonic compensation commands for the dynamic reactive power compensation devices, active filtering devices, and distributed energy inverters of each site; specifically including:

[0113] The design decision quantity is:

[0114]

[0115] Where u[k] is the control sequence from time k to k+N-1. For SVG, no power consumption. P is the reference for the h-th harmonic of the APF. l and Q l These are distributed energy resources with and without reactive power. For the harmonic reference of distributed energy, N is the number of rolling steps, T is the rolling time domain, and Δt is the sampling step size.

[0116] The objective function is designed as follows:

[0117]

[0118] Where J is the optimization objective, P st [j] represents short-time flicker, THD[j] represents total harmonic distortion, ΔU[j] represents voltage deviation, w1~w6 represent target weights, and c Q,i and c I,i,h These are the reactive power / harmonic power output cost coefficients for station i, and c. curt c is the cost coefficient for power curtailment. cycle Let (x) be the equivalent cost coefficient for the energy storage cycle. + =max(x,0), P av,l Active power is available for distributed energy.

[0119] Among them, P st [j] The calculation method is as follows: the instantaneous flicker sensation P output by the flicker meter is... inst (t), within the window for P inst (t) Perform quantile statistics and take P 0.1 P1, P3, P 10 P 50 Equal quantile values, combined according to standard weighting, yield:

[0120] ;

[0121] The following constraints need to be met:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] in, Rated reactive power for SVG, r Q For SVG ramp rate, The rated current for the h-th harmonic of the APF. The PQ capability curve for distributed energy inverters. This refers to the harmonic rated current for distributed energy resources.

[0129] The specific process for solving the multi-objective rolling optimization is as follows:

[0130] First, set the sampling period, window length, and event trigger threshold when powering on;

[0131] Each cycle first reads the latest measurement and performs verification, detecting whether events such as train arrival / departure, sudden increase in regenerative braking, or power quality approaching the threshold occur, and decides whether to activate "fast response" and adjusts the window length and weight accordingly.

[0132] Then, the short-term forecast is refreshed and the equivalent / sensitivity is updated near the current operating conditions;

[0133] Window the original objective and solve it;

[0134] After a successful solution, only the control command for the first step within the window is sent to the SVG / APF / distributed energy source to monitor the response and indicator convergence.

[0135] Finally, the time window is moved forward by one cycle, and the execution and deviation statistics are written to the log for subsequent adaptive fine-tuning. Then, the next round begins, and this process continues until the process exits.

[0136] To better suit subway operating conditions, a combined mechanism of "periodic triggering + event triggering" is adopted:

[0137] Periodic triggering involves performing rolling optimization once every Δt to ensure continuity.

[0138] Event triggering occurs when events such as train arrival / departure, grid connection point voltage deviation or THD threshold exceeding, SVG / APF state switching, or sudden changes in distributed energy output occur, immediately triggering a rapid scrolling event. The event triggering and rapid window / weight reconfiguration are as follows:

[0139]

[0140]

[0141] in, This is the event trigger indicator; it is 1 if the condition is met, and 0 otherwise. The measured voltage deviation at the grid connection point. , These are the event thresholds for voltage deviation and THD, respectively. For detecting operational events such as train arrival / departure. , , These are the window length, smoothing weight, and time weight that take effect temporarily after the event is triggered. , , This is the event scaling factor.

[0142] Considering train density during different operating periods, adaptive weights for subway operating conditions are set:

[0143]

[0144] in, Based on time weighting, This is the weighted sensitivity coefficient. The train density is based on time k. for The moving average over a period of time is used as the normalization benchmark.

[0145] When performing multi-objective rolling optimization, window the objectives and perform time summation and motion smoothing:

[0146]

[0147] in, Let k be the target value for the rolling window starting at time k. The time weight of step j within the window. For the stage goal of step j, Let j be the state vector at step j. Let γ be the control vector at step j, and let γ be the action smoothing weight, used to suppress frequent or excessive control jumps.

[0148] This invention solves power quality problems such as voltage deviation, harmonic exceedance, and flicker caused by frequent train starts and stops and power fluctuations of distributed energy sources through inter-station collaboration and rapid response of multiple devices.

[0149] This embodiment also provides a rapid governance system for collaborative rolling optimization of subway power quality, including distributed energy resources, comprising:

[0150] The data acquisition unit is used to acquire key data on the actual operation of the subway.

[0151] The prediction unit predicts traction power, AC measured power, and harmonics based on key data from actual operation.

[0152] The fundamental voltage deviation and total harmonic distortion calculation unit, based on the predicted traction power and AC measured power and harmonics, uses the sensitivity characterizing the relationship between each station and distributed energy source on the power quality of the 110kV side of the power grid to determine the fundamental voltage deviation and total harmonic distortion.

[0153] The multi-objective rolling optimization solution unit constructs decision quantities, objective functions, and constraints based on fundamental voltage deviation and total harmonic distortion. Based on a composite mechanism of periodic triggering and event triggering, it performs multi-objective rolling optimization to obtain reactive power and harmonic compensation commands for the dynamic reactive power compensation devices, active filtering devices, and distributed energy inverters of each site for dynamic coordination and scheduling.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A rapid method for collaborative rolling optimization of subway power quality, including distributed energy resources, characterized in that: include: Step S1: Obtain key data on actual subway operation; Step S2: Based on key data from actual operation, predict traction power, AC measured power, and harmonics; Step S3: Based on the predicted traction power, measured AC power, and harmonics, the fundamental voltage deviation and total harmonic distortion are determined by using the sensitivity of the relationship between each station and distributed energy source on the power quality of the 110kV side of the power grid. Step S4: Based on the fundamental voltage deviation and total harmonic distortion, construct the decision quantity, objective function and constraints, and perform multi-objective rolling optimization to obtain the reactive power and harmonic compensation commands for the dynamic reactive power compensation device, active filter device and distributed energy inverter of each site for dynamic coordination and scheduling.

2. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 1, characterized in that: The key data for actual subway operation mentioned in step S1 includes train timetables, real-time train positions and speeds (v). m [k] DC side rated voltage U dc AC side rated line voltage U LL Rated voltage U at grid connection point n The capacity of the dynamic reactive power compensation device for the AC busbars of each traction substation With the slope rate r Q Rated harmonic current compensation capacity of active filter device Reactance and equivalent resistance parameters, measurement and communication cycle Δt and rolling time domain T, and available active power prediction and reactive power curve of grid-connected distributed energy units.

3. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 1, characterized in that: The predicted traction power is: ； in, Let m be the traction power of the m-th train at time k. m Let a be the mass of the m-th train. m [k] represents the acceleration of the m-th train at time k, v m [k] represents the speed of the m-th train at time k, θ m [k] represents the gradient angle at time k where the m-th train is located, and c r Where ρ is the rolling resistance coefficient, ρ is the air density, and C is the rolling resistance coefficient. d Where A is the drag coefficient, g is the frontal area, and η is the acceleration due to gravity. m [k] represents the traction efficiency; All the power of the trains in the network By allocating the DC resistance network to the i-th traction substation, we obtain: ； ； ； Among them, P dc,i [k] and I dc,i [k] represents the power and current on the DC side of station i, respectively, and β i,m [k] is the power sharing coefficient from train m to station i, U dc R is the rated voltage on the DC side. i,m [k] represents the equivalent resistance of the corresponding circuit.

4. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 3, characterized in that: Predicting AC power and harmonics includes: Predicted AC active power is: ； Among them, P i [k] represents the active power of station i on the AC side, η tr,i For transformer efficiency, η rec,i For rectifier efficiency; Predicted AC reactive power is: ； ； ； Where, μ i [k] is the commutation overlap angle, ω is the angular frequency, and L eq,i For the equivalent commutation reactance, U Φ,i PF is the effective value of the phase voltage. i [k] represents the power factor, THD. I,i [k] represents the total harmonic distortion of the AC side current, Q i [k] represents the reactive power on the AC side of station i; The harmonics are: ； Among them, I h,i [k] represents the intensity of the h-th harmonic current, k h,i [k] is the h-th proportionality coefficient related to the load rate, and the set is the set of 24 pulse characteristics and residual harmonics.

5. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 4, characterized in that: The fundamental voltage deviation is: ； Where ΔU[k] is the bus voltage deviation at the 110kV grid connection point, S UQ,i The sensitivity of reactive power to voltage at station i. For the net reactive power injection of station i, S UIh,i,h To determine the sensitivity of the h-th harmonic current of station i to voltage, S UP,l For the active power sensitivity to voltage of the l-th distributed energy unit, P l [k] represents the active power output of the l-th distributed energy unit; The total harmonic distortion is: ； ； Among them, U h [k] represents the h-th harmonic voltage, H i,h Injecting sensitivity to the h-th harmonic voltage into station i. H is the net harmonic current of station i. l,h The sensitivity of the distributed energy unit to the h-th harmonic voltage. U1[k] is the reference for the h-th harmonic current of the distributed energy inverter, and U1[k] is the fundamental voltage.

6. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 5, characterized in that: The construction of decision variables, objective function, and constraints includes: The decision-making quantity is: ； Where u[k] is the control sequence from time k to k+N-1, For SVG, no power consumption. P is the reference for the h-th harmonic of the APF. l and Q l These are distributed energy resources with and without reactive power. For the harmonic reference of distributed energy, N is the number of rolling steps, T is the rolling time domain, and Δt is the sampling step size; The objective function is constructed as follows: ； Where J is the optimization objective, P st [j] represents short-time flicker, THD[j] represents total harmonic distortion, ΔU[j] represents voltage deviation, w1~w6 represent target weights, and c Q,i and c I,i,h These are the reactive power / harmonic power output cost coefficients for station i, and c. curt c is the cost coefficient for power curtailment. cycle Let (x) be the equivalent cost coefficient for the energy storage cycle. + =max(x,0), P av,l The short-time flicker P is available as active power for distributed energy; st [j] represents the instantaneous flicker perception P of the flicker meter output within the window. inst (t) Perform quantile statistics and take P 0.1 P1, P3, P 10 P 50 Equal quantile values ​​were then weighted according to standard parameters. The constraints are as follows: ； ； ； ； ； ； in, Rated reactive power for SVG, r Q For SVG ramp rate, The rated current for the h-th harmonic of the APF. The PQ capability curve for distributed energy inverters. This refers to the harmonic rated current for distributed energy resources.

7. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 1, characterized in that: A combined mechanism of periodic triggering and event triggering is employed for multi-objective rolling optimization; wherein: The periodic triggering mechanism performs a multi-objective rolling optimization solution once every Δt. The event triggering mechanism is as follows: when events such as train entering / leaving the station, grid connection point voltage deviation or THD threshold exceeding, SVG / APF state switching, or sudden change in distributed energy output occur, an additional multi-objective rolling optimization solution is immediately added. The event triggering and window / weight reconfiguration are as follows: ； ； in, This is an event trigger indicator; it is 1 if the condition is met, and 0 otherwise. The measured voltage deviation at the grid connection point. , These are the event thresholds for voltage deviation and THD, respectively. For detecting train arrival / departure station operation events. , , These represent the temporary window length, smoothing weight, and time weight that take effect after the event is triggered, respectively. , , This is the event scaling factor. Adaptive weights for subway operating conditions.

8. The rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 7, characterized in that: The adaptive weight of subway operating conditions for: ； in, Based on time weighting, This is the weighted sensitivity coefficient. The train density is based on time k. for The moving average over a period of time is used as the normalization benchmark.

9. A rapid governance method for collaborative rolling optimization of subway power quality with distributed energy sources according to claim 7, characterized in that: When performing multi-objective rolling optimization, the target is windowed, and time summation and motion smoothing are performed to obtain the target value of the rolling window: ； in, Let k be the target value for the rolling window starting at time k. The time weight of step j within the window. The stage goal for step j is... Let j be the state vector at step j. Let γ be the control vector at step j, and let γ be the action smoothing weight, used to suppress frequent or excessive control jumps.

10. A rapid governance system for collaborative rolling optimization of subway power quality, implementing the method of any one of claims 1-9, characterized in that: include: The data acquisition unit is used to acquire key data on the actual operation of the subway. The prediction unit predicts traction power, AC measured power, and harmonics based on key data from actual operation. The fundamental voltage deviation and total harmonic distortion calculation unit, based on the predicted traction power and AC measured power and harmonics, uses the sensitivity characterizing the relationship between each station and distributed energy source on the power quality of the 110kV side of the power grid to determine the fundamental voltage deviation and total harmonic distortion. The multi-objective rolling optimization solution unit constructs decision quantities, objective functions, and constraints based on fundamental voltage deviation and total harmonic distortion. Based on a composite mechanism of periodic triggering and event triggering, it performs multi-objective rolling optimization to obtain reactive power and harmonic compensation commands for the dynamic reactive power compensation devices, active filtering devices, and distributed energy inverters of each site for dynamic coordination and scheduling.

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