A micro-grid grid-connected and off-grid switching network type energy storage power coordination control method and system based on coordinated reactive power control

Abstract

A micro-grid parallel and off-grid switching network type energy storage power coordination control method and system based on coordinated reactive power control, the method comprising: selecting micro-grid operation data to be collected in real time for identifying parallel and off-grid switching transient characteristics and judging micro-grid operation state; constructing a coordinated reactive power control framework based on an equivalent circuit model of parallel and off-grid switching; constructing a multi-objective optimization model taking reactive droop coefficient adjustment of each network type energy storage converter as an optimization variable and taking minimum transient voltage deviation and maximum reactive capacity utilization as targets; solving to generate an optimal reactive droop coefficient adjustment mapping table covering typical operating conditions; obtaining optimal reactive droop coefficient adjustment online, dynamically adjusting reactive droop coefficient and executing closed-loop feedback correction. The present application solves the problems of low utilization efficiency of network type energy storage reactive resources and insufficient system stability in the switching process, and improves the response speed and adaptive operation capability of micro-grid parallel and off-grid switching.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation and control technology, specifically to a method and system for coordinated power control of grid-connected and off-grid switching of grid-connected energy storage based on coordinated reactive power control, and particularly to an optimized control of power distribution and reactive power regulation during grid-connected and off-grid switching involving grid-connected energy storage, applicable to remote power supply and distribution network terminal microgrid systems. Background Technology

[0002] With the advancement of "dual-carbon" goals and the continuous increase in the proportion of new energy sources, microgrids, as an important carrier for integrating distributed energy and ensuring power supply reliability, have made their flexible switching capability a key technology for ensuring the safe and stable operation of the system. Grid-Forming Power Conversion Systems (GFM-PCS), by simulating the electromechanical transients of rotating electrical machines and exhibiting external voltage source characteristics, are core supporting units in the switching process and have been increasingly widely used in microgrids. In grid-connected / off-grid switching scenarios, grid-forming energy storage needs to be able to adaptively operate both on and off the grid to meet the power dispatching requirements under different operating conditions.

[0003] However, at the boundary of nonlinear switching between grid and off-grid systems, the existing control architecture still reveals engineering adaptability issues: the low-voltage lines of microgrids have relatively high impedance and inductance, and there is a natural coupling between active and reactive power flows. During the switching period, the rapid change in active power output of the grid-connected energy storage converter is accompanied by a shift in the virtual power angle, which introduces coupled disturbances into the reactive power channel under the impedance-inductance network. The existing reactive power droop control only responds to voltage amplitude deviations and cannot offset this disturbance. The reactive power regulation capability of grid-connected energy storage is not fully utilized, and there is a lack of coordination mechanism between reactive power commands and active power dispatch, resulting in insufficient system voltage support and increased reactive power circulating current during the switching period, affecting the stability of the switching process.

[0004] Therefore, there is an urgent need for a grid-based energy storage coordinated control technology for the grid-to-off-grid switching process, which integrates feature identification with active and reactive power synergistic optimization to reduce switching impact and improve the resilience of microgrid operation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for coordinated power control of grid-connected and off-grid switching of grid-connected energy storage based on coordinated reactive power control. By identifying transient characteristics of grid-connected and off-grid switching in real time, a multi-objective optimization model for power allocation and reactive power regulation of the grid-connected energy storage system is constructed. This dynamically optimizes the active power output allocation and reactive power regulation commands of the energy storage system, thereby solving the problems of low reactive power resource utilization efficiency and insufficient system stability of grid-connected energy storage during the switching process, and improving the response speed and adaptive operation capability of microgrid grid-connected and off-grid switching.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of this invention provides a coordinated control method for grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control, comprising the following steps: Select microgrid operation data that needs to be collected in real time to identify and handle transient characteristics during off-grid switching and determine the microgrid's operating status; Based on the equivalent circuit model of microgrid switching between grid and off-grid with grid-type energy storage, a collaborative reactive power control framework is constructed, including: voltage command conversion, reactive power allocation and active-reactive power coordination control. Based on the collaborative reactive power control framework, a multi-objective optimization model for power coordination of grid-type energy storage is constructed, with the reactive power droop coefficient adjustment of each grid-type energy storage converter as the optimization variable and the objectives of minimizing transient voltage deviation and maximizing reactive power capacity utilization. The multi-objective optimization model for power coordination of the grid-type energy storage is solved offline using a multi-objective optimization algorithm to generate an optimal reactive power droop coefficient adjustment mapping table covering typical operating conditions. The optimal reactive power droop coefficient adjustment amount is obtained by online table lookup, the reactive power droop coefficient is dynamically adjusted, and closed-loop feedback correction is performed.

[0008] Preferably, the selection of microgrid operation data to be collected in real time for identifying and handling off-grid switching transient characteristics and determining the microgrid's operating status includes: Real-time acquisition of electrical operation data at the grid connection point via hard-wired connection, including: instantaneous values ​​of three-phase voltage, instantaneous values ​​of three-phase current, and system frequency at the grid connection point. f Active power P reactive power Q and grid-connected switch auxiliary contact position signal S grid ,in, S grid = 1 indicates a closed loop. S grid = 0 indicates disconnection; Based on the acquired electrical operation data of the grid connection point, the following transient characteristic quantities are calculated in real time using a sliding time window: voltage amplitude change rate Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t ; The acquired transient characteristic quantity is compared with a preset threshold, and simultaneously combined with the auxiliary contact position signal of the grid-connected switch. S gridThe current value and historical value are used to determine the microgrid's operating state as grid-connected steady state, off-grid steady state, grid-connected to off-grid switching transient state, or off-grid to grid-connected switching transient state. Upon determining that a handover transient has been entered, the handover start time is immediately latched. t 0, and extract the start time of the switch. t Power surge peak Δ within the window before and after 0 P gap Total reactive power deficit Δ Q gap The impact direction is used as the constraint boundary input for the multi-objective optimization model.

[0009] Preferably, the construction of a collaborative reactive power control framework based on the equivalent circuit model of the microgrid with grid-type energy storage for grid-connected and off-grid switching includes: voltage command conversion, reactive power allocation, and active-reactive power coordination control. An equivalent circuit model of a microgrid with grid-connected energy storage and off-grid switching is constructed. The equivalent circuit model treats the grid-connected energy storage converter as a controlled voltage source and adopts a virtual synchronous generator control strategy. The active power control loop performs active power-frequency droop control, and the reactive power control loop performs reactive power-voltage droop control. Based on the established microgrid grid-connected / off-grid switching equivalent circuit model, a collaborative reactive power control framework is constructed. The voltage command conversion includes: generating a total reactive power support reference value based on the positive sequence voltage amplitude deviation at the grid connection point; the reactive power allocation includes: initial allocation based on the available reactive power capacity margin of each grid-type energy storage converter; and the active-reactive power coordinated control includes: updating the actual reactive power droop coefficient executed by each energy storage converter based on the reactive power droop coefficient adjustment amount output by the multi-objective optimization model.

[0010] Preferably, the multi-objective optimization model for power coordination of grid-type energy storage, based on a collaborative reactive power control framework and using the reactive power droop coefficient adjustment of each grid-type energy storage converter as the optimization variable, and aiming at minimizing transient voltage deviation and maximizing reactive power capacity utilization, includes: Using the reactive power droop coefficient adjustment amount of each grid-type energy storage converter as the optimization variable, the sum of the initial value of the reactive power droop coefficient based on the offline setting under rated operating conditions and the reactive power droop coefficient adjustment amount is taken as the actual reactive power droop coefficient. During the switching process t Voltage deviation Δ at time U ( t The integral over the duration of the switching process is the voltage deviation during the switching process, and thus a first objective function is constructed with the goal of minimizing the voltage deviation during the switching process. The ratio of the sum of the actual reactive power output of all grid-type energy storage converters to the maximum available reactive power capacity margin is the reactive power capacity utilization rate. A second objective function is constructed with the goal of maximizing the reactive power capacity utilization rate.

[0011] Preferably, based on the control equations of the grid-type energy storage converter and the system power balance relationship, the switching process in the first objective function is... t Voltage deviation Δ at time U ( t This is expressed as the reactive power droop coefficient adjustment amount Δ. D q,i The algebraic function is used to achieve optimization solutions.

[0012] Preferably, the actual reactive power output of each grid-type energy storage converter in the second objective function is expressed as the superposition of a droop response component and an active power coupling feedforward compensation component. The droop response component is generated based on the optimized, actually executed reactive power droop coefficient, and the active power coupling feedforward compensation component is based on the grid-type energy storage converter. i The feedforward compensation coefficient and the identified power impulse slope generate active coupling feedforward compensation components.

[0013] Preferably, constraints are set for the first objective function and the second objective function, including: boundary constraints on the reactive power droop coefficient adjustment, reactive power capacity limit constraints, and lower limit constraints on the total reactive power droop coefficient; The boundary constraint for the reactive power droop coefficient adjustment is that the adjustment amount of the reactive power droop coefficient of each grid-type energy storage converter is not lower than the lower limit of the adjustment amount and not higher than the upper limit of the adjustment amount. The reactive power limit is that the reactive power output or absorbed by each grid-type energy storage converter during the switching process does not exceed its available reactive power capacity margin. The lower limit constraint of the total reactive power droop factor is that the total reactive power droop factor shall not be lower than the total reactive power deficit or surplus and the allowable voltage deviation.

[0014] Preferably, the step of using a multi-objective optimization algorithm to solve the established multi-objective optimization model for power coordination of the grid-type energy storage offline, and generating an optimal reactive power droop coefficient adjustment mapping table covering typical operating conditions, includes: Transient features are selected as the working condition index dimension and discretized within their expected range of change. An improved NSGA-II algorithm was used as the multi-objective optimization algorithm to solve the established multi-objective optimization model for power coordination of grid-type energy storage converters. The index values ​​of all discrete operating points and their corresponding optimal reactive power droop coefficient adjustment values ​​are organized into a multidimensional lookup table.

[0015] Preferably, the step of obtaining the optimal reactive power droop coefficient adjustment amount through online table lookup, dynamically adjusting the reactive power droop coefficient, and performing closed-loop feedback correction includes: Read the currently calculated transient characteristics: total reactive power deficit, power surge peak, and switching start time. t 0 voltage deviation ΔU ( t 0) and statistical values ​​of available reactive power capacity margin for each grid-type energy storage converter; quickly calculate the reactive power droop coefficient adjustment amount corresponding to each grid-type energy storage converter; After being constrained by boundary limits, the reactive power droop coefficient is updated as the final adjustment amount. After the parameters are updated, the controller continuously monitors the voltage amplitude at the grid connection point and compares it with the expected response. When the set conditions are met, the controller performs parameter fine-tuning in finite steps. When entering the target steady-state operation mode, the current optimized reactive power droop coefficient is locked as the new steady-state reference value, the table lookup and feedback correction calculations are stopped, the switching optimization mode is exited, and the normal steady-state droop control is restored.

[0016] The second aspect of the present invention provides a coordinated control system for microgrid grid-connected and off-grid switching energy storage power based on coordinated reactive power control, which operates the coordinated control method for microgrid grid-connected and off-grid switching energy storage power according to the first aspect, including: an acquisition and identification module, a coordinated reactive power control framework module, a solver, a memory, and an output module; The acquisition and identification module is used to acquire microgrid operating status data and identify transient characteristics in real time. The collaborative reactive power control framework module is used to generate a total reactive power support benchmark value based on the grid connection point voltage deviation, perform initial allocation according to the reactive power margin of each energy storage unit, receive the reactive power droop coefficient adjustment amount output by the multi-objective optimization model, and update the reactive power droop coefficient actually executed by each energy storage unit. The solver is used to solve the multi-objective optimization model for power coordination of grid-type energy storage, and to generate the reactive power droop coefficient adjustment amount. The memory is used to store a multidimensional lookup table organized by the index values ​​of all discrete operating points and their corresponding optimal adjustment values. The output module is used to obtain the optimal adjustment amount by looking up a table online after the acquisition and identification module identifies that the system has entered a switching transient state and extracts the transient characteristic quantity, dynamically adjust the reactive power droop coefficient and perform closed-loop feedback correction.

[0017] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1) By identifying and switching transient characteristics in real time, the system can accurately determine the switching state, providing accurate timing references and constraints for subsequent optimized control, thereby improving the targeting and timeliness of the control.

[0018] 2) This invention uses reactive power capacity utilization as one of its optimization objectives. In the optimization solution, it fully considers the differences in available reactive power margin among each energy storage unit, guiding reactive power output towards units with ample margins and preventing some units from prematurely limiting their output due to reactive power overload. Compared to existing passive response methods that allocate reactive power according to a fixed ratio or rely solely on local voltage droop, this invention significantly improves the overall reactive power resource utilization efficiency of the system and alleviates the problem of uneven reactive power distribution during transient transitions.

[0019] 3) This invention embeds a feedforward compensation term based on the rate of change of active power into the reactive power control channel, actively offsetting the coupling disturbance of reactive power measurement caused by the shift in the power angle due to rapid adjustment of active power. This mechanism weakens the reactive power circulating current amplitude during multi-machine parallel switching from a physical mechanism perspective.

[0020] 4) This invention identifies transient characteristics of switching in real time and dynamically matches optimal control parameters, enabling grid-connected energy storage systems to adaptively cope with grid-connected / off-grid switching scenarios under different power deficits and reactive power impact levels. A closed-loop feedback correction mechanism is introduced to correct the control strategy in real time based on the deviation between the actual response curve and the target value, enhancing the adaptability of the control strategy to changes in operating conditions. Attached Figure Description

[0021] Figure 1 This is an overall flowchart of the method of the present invention; Figure 2 A logic diagram for generating reactive power reference values ​​for grid-type energy storage is provided, which describes the logic for generating reactive power reference values ​​for grid-type energy storage. Detailed Implementation

[0022] 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 described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0023] like Figure 1 As shown, Embodiment 1 of the present invention provides a coordinated control method for grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control. The specific steps are as follows: Step 1: Collect microgrid operation data in real time, identify and detect transient characteristics of off-grid switching, and determine the microgrid operation status.

[0024] Preferably, but not limitingly, step 1 specifically includes: Step 1.1: Real-time acquisition of electrical operation data at the grid connection point via hard-wiring. This electrical operation data includes: instantaneous values ​​of the three-phase voltage, instantaneous values ​​of the three-phase current, and system frequency at the grid connection point. fActive power P reactive power Q And grid-connected switch auxiliary contact position signal S grid ,in, S grid = 1 indicates a closed loop. S grid = 0 indicates disconnection.

[0025] Step 1.2: Based on the electrical operation data of the grid connection point obtained in Step 1.1, identify and determine the transient characteristics of the off-grid switching and the system operating status.

[0026] Further preferred, but not limiting, step 1.2 specifically includes: Based on the electrical operation data of the grid connection point obtained in step 1.1, the following transient characteristic quantities are calculated in real time using a sliding time window: voltage amplitude change rate Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t Preferably, the time window length is 10~20ms, and more preferably, the time window length is 10ms.

[0027] The acquired transient characteristic quantity is compared with a preset threshold, and simultaneously combined with the auxiliary contact position signal of the grid-connected switch. S grid The current and historical values ​​are compared to determine the system's operating status based on the following criteria: like S grid = 1 and the rate of change of voltage amplitude Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t All values ​​were below the threshold, and the duration exceeded the steady-state time threshold. T steady Then the operating state is grid-connected steady state; like S grid = 0 and the rate of change of voltage amplitude Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t All values ​​were below the threshold, and the duration exceeded the steady-state time threshold. T steady Then the operating state is the off-grid steady state; like S grid The voltage changes from 1 to 0 and the rate of change of voltage amplitude Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t If at least two of the values ​​exceed the threshold, the operating status is a grid-to-off-grid switching transient. like S grid The voltage changes from 0 to 1 and the rate of change of voltage amplitude Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t If at least two of the values ​​exceed the threshold, the operating status is a transient state of switching from off-grid to on-grid. The preset thresholds are adaptively and dynamically adjusted based on the short-circuit capacity of the grid connection point and the current operating power level—reducing the allowable fluctuation range of frequency and voltage under heavy load conditions, and more sensitively capturing switching transient characteristics in the early stages of disturbances; preferably, the voltage change rate threshold is 0.05 pu / s, the frequency change rate threshold is 0.1 Hz / s, and the power change rate threshold is 0.1 pu / s; the steady-state time threshold... T steady It takes 20ms.

[0028] Step 1.3: After determining in Step 1.2 that the system has entered a switching transient state, immediately latch the switching start time. t 0, and extract the start time of the switch. t Power surge peak Δ within the window before and after 0 P gap Total reactive power deficit Δ Q gap The impact direction is used as the constraint boundary input for the subsequent optimization model.

[0029] Step 2: Based on the equivalent circuit model of the microgrid with grid-type energy storage switching between grid and off-grid, construct a collaborative reactive power control framework, including: voltage command conversion, reactive power allocation, and active-reactive power coordinated control.

[0030] Preferably, but not limitingly, step 2 specifically includes: Step 2.1: Construct an equivalent circuit model of a microgrid with grid-type energy storage and off-grid switching. The equivalent circuit model treats the grid-type energy storage converter as a controlled voltage source and adopts a virtual synchronous generator control strategy. The active power control loop performs active power-frequency droop control, and the reactive power control loop performs reactive power-voltage droop control.

[0031] The active-frequency droop control expression is as follows:

[0032] In the formula: P ref This is a reference value for active power. P out This refers to the actual output active power; D p This is the active power droop coefficient; ω For virtual angular frequency, ω 0 is the rated angular frequency; J This is virtual inertia.

[0033] The reactive power-voltage droop control expression is:

[0034] In the formula: Q ref This is a reference value for reactive power. Q out This refers to the actual output reactive power. D q This is the reactive power droop coefficient; U The output voltage amplitude, U 0 represents the rated voltage amplitude.

[0035] Preferably, virtual inertia J =5kg·m², active sagging coefficient D p =20kW / Hz, reactive power droop factor D q =10kVar / V.

[0036] Step 2.2: Based on the equivalent circuit model of the microgrid switching between grid and off-grid established in Step 2.1, construct a collaborative reactive power control framework, including: voltage command conversion, reactive power allocation, and active-reactive power coordinated control.

[0037] The voltage command conversion includes: generating a total reactive power support reference value based on the positive sequence voltage amplitude deviation at the grid connection point; specifically, using the positive sequence voltage amplitude deviation Δ at the grid connection point... U = U ref U meas As input, a grid-type energy storage converter is generated through reactive power-voltage droop characteristics. i Initial value of reactive power reference Q i (0), where, i =1,2,…… n , n The number of grid-connected energy storage converters, U ref This is the reference value for the positive sequence voltage at the grid connection point. U meas This is the measured value of the positive sequence voltage at the grid connection point.

[0038] The reactive power allocation includes: the reactive power allocation layer performs initial allocation based on the available reactive power capacity margin of each grid-type energy storage converter; specifically, the grid-type energy storage converter that receives voltage command conversion output... i Initial value of reactive power reference Q i (0), and the grid-type energy storage converter is obtained by dynamic calculation of the apparent power rating of the converter, the current active power output and reactive power output. i Real-time available reactive power capacity margin Qavail i The reactive power of each grid-type energy storage converter is redistributed according to the principle of equal margin. Q out,i .

[0039] The active-reactive power coordination control includes: the power coordination layer receiving the reactive power droop coefficient adjustment amount output by the multi-objective optimization model in step 3, and updating the reactive power droop coefficient actually executed by each energy storage converter accordingly.

[0040] Step 3: Based on the switching transient characteristics identified in Step 1, construct a grid-type energy storage converter. i The adjustment amount of reactive power droop coefficient Δ D q,i To optimize variables, a multi-objective optimization model for power coordination of grid-type energy storage is proposed, with the objectives of minimizing transient voltage deviation and maximizing reactive capacity utilization.

[0041] It is worth noting that in step 2, the initial values ​​of various control parameters, especially the reactive power droop coefficient, are crucial. D q,i0 Typically, these parameters are set offline based on rated operating conditions. However, during the grid-connected / off-grid switching transient process, the system operating point shifts drastically, making it difficult for these static parameters to adapt to dynamic demands. As one of the prominent substantive features of this invention, step 3 uses the initial control parameters output in step 2 as a benchmark and the switching transient characteristic quantities identified in step 1 as boundary inputs to establish the following optimization model, solving for the reactive power droop coefficient adjustment of each grid-type energy storage converter. D q,i .

[0042] Step 3.1: Using grid-type energy storage converters i The adjustment amount of reactive power droop coefficient Δ D q,i To optimize the variables, the sum of the initial value of the reactive power droop coefficient based on offline tuning under rated operating conditions and the adjustment amount of the reactive power droop coefficient is taken as the actual reactive power droop coefficient, expressed by the following formula: D q,i = D q,i0 +Δ D q,i In the formula: D q,i0 Δ D q,i and D q,i These are grid-type energy storage converters i The initial value of the reactive power droop coefficient, the adjustment amount of the reactive power droop coefficient, and the actual reactive power droop coefficient.

[0043] Step 3.2: During the switching process t Voltage deviation Δ at time U ( t The integral over the duration of the switching process is the switching process voltage deviation. Therefore, a first objective function is constructed with the goal of minimizing the switching process voltage deviation, expressed by the following formula:

[0044] In the formula: f 1 represents the voltage deviation during the switching process; t 0 represents the start time of the switching process. T The duration of the switching process; Δ U ( t (This refers to the handover process) t Voltage deviation at any moment; To achieve optimized solution, the switching process in the above integral first objective function... t Voltage deviation Δ at time U ( t Based on the control equations of the grid-type energy storage converter and the system power balance relationship, it can be approximately expressed as an algebraic function of the optimization variables, namely the reactive power droop coefficient adjustment Δ. D q,i Algebraic functions. Specifically, during the switching process. t Voltage deviation Δ at time U ( t The integral of ) and the adjustment amount Δ of reactive power droop coefficient D q,i The relationship is expressed by the following formula:

[0045] In the formula: Δ Q gap The instantaneous total reactive power deficit is identified in step 1; D q,i0 and Δ D q,i These are grid-type energy storage converters i The initial value of the reactive power droop coefficient and the adjustment amount of the reactive power droop coefficient. i =1,2,…… n , n The number of grid-type energy storage converters.

[0046] Step 3.3: For the entire switching process, the ratio of the sum of the actual reactive power outputs of all grid-connected energy storage converters to the maximum available reactive power capacity margin is the reactive power capacity utilization rate. A second objective function is constructed with the goal of maximizing the reactive power capacity utilization rate, expressed by the following formula:

[0047] In the formula: f 2 represents reactive power capacity utilization rate; Q out,i ( t ) is a grid-type energy storage converter i The actual unproductive output; Qavail i ( t ) is a grid-type energy storage converter i Available reactive power capacity margin.

[0048] Among them, grid-type energy storage converter i Actual reactive power Qout,i ( t ) by droop response component Q droop,i ( t ) and active power coupling feedforward compensation components Q comp,i ( t It is formed by superposition, and can be expressed by the following formula:

[0049] In the formula: Q ref,i For grid-type energy storage converter i Reference value for reactive power output; K comp,i For grid-type energy storage converter i The feedforward compensation coefficient is typically taken as a value related to the line impedance-inductance ratio, such as, but not limited to, K comp,i = - τ i R i / X i ,in, R i / X i For grid-type energy storage converter i The line resistance-to-inductance ratio, τ i For grid-type energy storage converter i The filtering time constant; Δ U ( t (This refers to the handover process) t The voltage deviation at any given time, as described in step 3.2, can be determined by the total reactive power deficit Δ. Q gap Expressed as a ratio to the total droop factor; dP out,i / dt The power impulse slope that can be identified from step 1 is approximated.

[0050] like Figure 2 As shown, the voltage deviation is obtained through droop adjustment. Q droop After being summed with the feedforward compensation amount based on the rate of change of active power, the final value is formed by capacity and voltage limiting. Q ref The droop coefficient is among them. D q From steady-state value Dq0 Δ from the optimization layer output D q We jointly decide to achieve dynamic collaborative optimization during the switching process.

[0051] Step 3.4: Set constraints for the first objective function and the second objective function, including: boundary constraints for reactive power droop coefficient adjustment, reactive power capacity limit constraints, and lower limit constraints for total reactive power droop coefficient.

[0052] The boundary constraint of the reactive power droop coefficient adjustment is for a grid-type energy storage converter. i The adjustment amount of the reactive power droop coefficient shall not be lower than the lower limit of the adjustment amount and not higher than the upper limit of the adjustment amount, as expressed by the following formula: Δ D q,imin ≤ Δ D q,i ≤ Δ D q,imax In the formula: Δ D q,imin For grid-type energy storage converter i The lower limit of the reactive power droop coefficient adjustment; Δ D q,i For grid-type energy storage converter i The adjustment amount of reactive power droop coefficient; Δ D q,imax For grid-type energy storage converter i The maximum adjustment limit for the reactive power droop coefficient.

[0053] The reactive power capacity limit is t Real-time grid-type energy storage converter i The reactive power output or absorption shall not exceed its available reactive power capacity margin, as expressed by the following formula:

[0054] In the formula: Q out,i ( t )for t Real-time grid-type energy storage converter i reactive power; Qavail i ( t )for t Real-time grid-type energy storage converter i Available reactive power capacity margin.

[0055] The lower limit constraint of the total reactive power droop factor is that the total reactive power droop factor shall not be lower than the total reactive power deficit or surplus and the allowable voltage deviation, expressed by the following formula:

[0056] In the formula: D q,i0 For grid-type energy storage converter i The initial value of the reactive power droop coefficient, Δ D q,i For grid-type energy storage converter i The adjustment amount of the reactive power droop coefficient i =1,2,…… n , n The number of grid-type energy storage converters; Δ Q gap This represents the total reactive power deficit. Δ U allow This refers to the allowable voltage deviation.

[0057] It is worth noting that, as one of the prominent substantive features of this invention, step 3 adjusts the reactive power droop coefficient Δ of each grid-type energy storage unit. D q,i To optimize variables, the optimization objective balances voltage quality and reactive power utilization efficiency during the switching process. Preferably, the specific parameters are set as follows: switching process duration... T Take 500ms; the boundary constraint for the reactive power droop coefficient adjustment is Δ. D q,i ∈[ 5, +10] kVar / V; allowable voltage deviation is Δ U allow =0.05pu; the circuit resistance-to-inductance ratio is taken as... R / X =0.8.

[0058] Step 4: During the system debugging phase before commissioning, use a host computer or simulation platform to solve the multi-objective optimization model for power coordination of the grid-type energy storage established in Step 3 offline using a multi-objective optimization algorithm. Generate an optimal reactive power droop coefficient adjustment mapping table covering typical operating conditions for quick online operation.

[0059] Preferably, but not limitingly, step 4 specifically includes: Step 4.1: Discretization of the operating condition space, including: selecting the following transient characteristic quantities identified in Step 1 as the operating condition index dimensions, and discretizing them within their expected range of change: total reactive power deficit Δ Q gap Discretized intoN q Each interval; power impact slope Δ P gap / dt Discretized into N p Each interval; voltage deviation Δ before switching U ( t 0), discrete as N u Each energy storage unit can use the minimum or average value of its reactive power capacity margin as a margin characteristic quantity, and discretize it into intervals. N m A range.

[0060] Step 4.2: Multi-objective optimization solution, including: using the improved NSGA-II (Non-dominated Sorting Genetic Algorithm II, an improved non-dominated sorting genetic algorithm with elitist strategy) as the multi-objective optimization algorithm to solve the power coordination multi-objective optimization model of the grid-type energy storage converter established in Step 3.

[0061] Preferably, the solution process includes: 1) Initialize the population, and encode each individual as containing the reactive power droop coefficient adjustment Δ of each grid-type energy storage unit. D q,i ; 2) Determine the constraints: boundary constraints for reactive power droop coefficient adjustment of grid-type energy storage, reactive power capacity limit, and total reactive power droop coefficient constraint; 3) Calculate the multi-objective function value for each individual, and perform non-dominated ranking and crowding calculation; 4) Generate a new generation of population through selection, crossover, and mutation operations, and iterate until the convergence condition is met; 5) From the Pareto front solution set, select the optimal compromise solution of the first objective function and the second objective function based on the fuzzy membership function, and output the optimal reactive power droop coefficient adjustment amount. .

[0062] More preferably, during the system debugging phase, operational improvements are implemented. NSGA - II The algorithm is used to solve this problem, with a population size of 50 and a maximum number of iterations of 100. It iterates through the following operating conditions: total reactive power deficit Δ... Qgap(0~100kVar, step size 10kVar), power surge slope (0~50kW / s, step size 5kW / s), voltage deviation before switching (-5%~+5%, step size 1%), minimum available reactive power margin (10~50kVar, step size 5kVar). For each discrete operating point, solve the multi-objective optimization model in step 3, selecting a compromise solution from the Pareto front that prioritizes voltage deviation while also considering reactive power capacity utilization. Record the optimal reactive power droop coefficient adjustment. .

[0063] Step 4.3: Mapping table generation and storage, including: index values ​​of all discrete operating points and their corresponding optimal adjustment values. The system is organized as a multidimensional lookup table, stored in the non-volatile memory of the central controller or each local controller in the network-type energy storage system. For operating conditions not directly covered in the table, an approximate optimal solution is obtained through multidimensional linear interpolation during online operation.

[0064] Step 5: After Step 1 identifies the system entering a switching transient and extracts transient characteristic quantities, the optimal adjustment value is obtained by online table lookup, the reactive power droop coefficient is dynamically adjusted, and closed-loop feedback correction is performed. When Step 1 determines that the system has entered a switching transient, the controller immediately reads the current reactive power deficit Δ. Q gap Power surge peak Δ P gap The available reactive power margin is used to obtain the adjustment amount Δ of each unit through multidimensional linear interpolation in the mapping table. D q,i After boundary limiting, the droop coefficient is updated. When step 1 detects that both the voltage and frequency change rates have fallen below the threshold and the switching state is stable, the current state is locked. Dq , i As the new steady-state benchmark, exit the switching optimization mode and resume normal steady-state droop control.

[0065] Preferably, but not limitingly, step 5 specifically includes: Step 5.1: Table lookup and interpolation, including: reading the currently calculated transient characteristic: total reactive power deficit Δ Q gap Power surge peak Δ P gap Switch start time t 0 voltage deviation Δ U ( t 0) and the available reactive power margin statistics of each grid-type energy storage converter. Locate the grid point closest to the current index in the offline stored mapping table, and quickly calculate the grid-type energy storage converter using multidimensional linear interpolation or inverse distance weighted interpolation methods. i The corresponding reactive power droop coefficient adjustment amount Δ Dq,i .

[0066] Step 5.2: Parameter limiting and updating, including: after the interpolation result is limited by the boundary constraints described in step 3, the actual reactive power droop coefficient is updated as the final adjustment amount.

[0067] Step 5.3: Closed-loop feedback correction, including: after parameter update, the controller continuously monitors the voltage amplitude at the grid connection point. U meas ( t And compare it with the expected response: Let the voltage deviation dead zone be... εdeadU When |Δ U |> εdeadU Each time, a parameter fine-tuning with a finite step size is triggered. Fine-tuning amount δD q,i Calculate using the following formula:

[0068] In the formula: η To fine-tune the gain, the adjusted parameters are sent back to the converter.

[0069] Step 5.4: Exit the switching optimization mode, including: when Step 1 determines that the system has entered the target steady-state operation mode (voltage change rate and frequency change rate are consistently below the threshold, and the switching state is stable), lock the currently optimized reactive power droop coefficient. D q,i As a new steady-state reference value (or smoothly restored to the original reference value according to preset rules) D q,i0 Stop table lookup and feedback correction calculations, exit the switching optimization mode, and resume normal steady-state droop control.

[0070] Embodiment 2 of the present invention provides a microgrid grid-connected and off-grid switching energy storage power coordination control system based on cooperative reactive power control, which runs the microgrid grid-connected and off-grid switching energy storage power coordination control method according to Embodiment 1, including: a data acquisition and identification module, a cooperative reactive power control framework module, a solver, a memory, and an output module.

[0071] The acquisition and identification module is used to acquire microgrid operating status data and identify transient characteristics in real time. The collaborative reactive power control framework module is used to generate a total reactive power support benchmark value based on the grid connection point voltage deviation, perform initial allocation according to the reactive power margin of each energy storage unit, receive the reactive power droop coefficient adjustment amount output by the multi-objective optimization model, and update the reactive power droop coefficient actually executed by each energy storage unit. The solver is used to solve the multi-objective optimization model for power coordination of grid-type energy storage, and to generate the reactive power droop coefficient adjustment amount. The memory is used to store a multidimensional lookup table organized by the index values ​​of all discrete operating points and their corresponding optimal adjustment values. The output module is used to obtain the optimal adjustment amount by looking up a table online after the acquisition and identification module identifies that the system has entered a switching transient state and extracts the transient characteristic quantity, dynamically adjust the reactive power droop coefficient and perform closed-loop feedback correction.

[0072] To more clearly illustrate the outstanding substantive features of this invention and the significant progress it brings to the prior art, an application example of implementing this invention is described below.

[0073] This embodiment focuses on an AC microgrid system incorporating grid-connected energy storage. The system includes two parallel-operating grid-connected energy storage converters (GFM-ESS1 and GFM-ESS2), photovoltaic power generation units, and AC loads. The grid-connected energy storage employs a virtual synchronous generator control strategy, with a rated active power of 100kW and a rated reactive power capacity of ±50kVar for each unit. The grid connection point is connected to the public distribution network via a static transfer switch. The system's rated voltage is 400V, and its rated frequency is 50Hz.

[0074] Step 1: Real-time data acquisition and transient feature identification Real-time acquisition of instantaneous three-phase voltage, instantaneous three-phase current, system frequency, active power, and reactive power at the grid connection point, while simultaneously acquiring the position signals of the auxiliary contacts of the grid-connected switch. Grid ( Grid =1 indicates a closed loop. Grid =0 indicates disconnection.

[0075] With 10 Ms. Calculate the rate of change of voltage amplitude Δ using a sliding time window. U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t The preset threshold values ​​are: voltage change rate threshold 0.05 pu / s, frequency change rate threshold 0.1 Hz / s, and power change rate threshold 0.1 pu / s. When any two change rate indicators exceed their respective thresholds and the duration exceeds 20 ms, combined with the current and historical values ​​of the switch position signal, the system is determined to have entered a switching transient state, and the start time of the switching is recorded. t 0. Power surge peak Δ P gap Total reactive power deficit Δ Q gap And the direction of impact.

[0076] Step 2: Establish the equivalent circuit model and cooperative control framework Grid-based energy storage uses VSG control, with a virtual inertia J = 5 kg·m² and an active power droop coefficient. D p =20kW / Hz, reactive power droop factor D q =10kVar / V. In the collaborative reactive power control framework, the total reactive power support benchmark value is generated based on the grid connection point voltage deviation; the reactive power allocation layer performs initial allocation according to the reactive power margin of each energy storage unit; the power coordination layer receives the dynamic correction amount output by the multi-objective optimization model in step 3, and updates the reactive power droop coefficient actually executed by each energy storage unit accordingly.

[0077] Step 3: Establish a multi-objective optimization model This step adjusts the reactive power droop coefficient Δ of each grid-type energy storage unit. D q,i To optimize variables and achieve an optimization objective that balances voltage quality and reactive power utilization efficiency during the switching process, the specific parameter settings are as follows: Switching process duration. T Take 500 ms; the boundary constraint for the reactive power droop coefficient adjustment is Δ. D q,i ∈[ 5, +10] kVar / V; allowable voltage deviation is Δ U allow =0.05pu; the line resistance-to-inductance ratio is taken as R / X=0.8.

[0078] Step 4: Offline optimization of table building and multi-objective optimization solution During the system debugging phase, the improved NSGA-II algorithm was run to solve the problem, with a population size of 50 and a maximum number of iterations of 100. The following operating conditions were traversed: total reactive power deficit ΔQgap (0~100kVar, step size 10kVar), power surge slope (0~50kW / s, step size 5kW / s), voltage deviation before switching (-5%~+5%, step size 1%), and minimum available reactive power margin (10~50kVar, step size 5kVar). For each discrete operating condition, the multi-objective optimization model from step 3 was solved, and a compromise solution prioritizing voltage deviation was selected from the Pareto front. The optimal adjustment amount ΔQgap was recorded. D * q,i .

[0079] Step 5: Online table lookup and parameter update Once step 1 determines that the system has entered a switching transient state, the controller immediately reads the current Δ. Q gap Δ P gap Δ U (t 0) and minimum available reactive power margin, and obtain the adjustment amount Δ of each unit through multidimensional linear interpolation in the mapping table. D q,i After boundary limiting, the droop coefficient is updated. When step 1 detects that both the voltage and frequency change rates have fallen below the threshold and the switching state is stable, the current state is locked. D q,i As the new steady-state benchmark, exit the switching optimization mode and resume normal steady-state droop control.

[0080] Comparative tests were conducted on grid-connected to off-grid switching using both the fixed droop coefficient method and the method of this invention. Test results show that, using the method of this invention, the maximum voltage drop during switching was reduced from 0.18 pu to 0.08 pu, the recovery time was shortened from 320 ms to 180 ms, and the peak parallel circulating current was reduced from 85 A to 28 A. No oscillations or disconnections occurred in the system throughout the switching process, verifying the effectiveness and superiority of the method of this invention.

[0081] It is worth noting that, in the embodiments of the present invention, the steps plus numbers are merely a way of expressing the specific implementation of the microgrid on-grid and off-grid switching grid-type energy storage power coordination control method based on coordinated reactive power control, rather than an absolute restriction on the order of the steps. Under the guidance of the core concept of the present invention, changing the order of these steps to achieve the same or similar technical effects all fall within the scope of the present invention.

[0082] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control, characterized in that, Includes the following steps: Select microgrid operation data that needs to be collected in real time to identify and handle transient characteristics during off-grid switching and determine the microgrid's operating status; Based on the equivalent circuit model of microgrid switching between grid and off-grid with grid-type energy storage, a collaborative reactive power control framework is constructed, including: voltage command conversion, reactive power allocation and active-reactive power coordination control. Based on the collaborative reactive power control framework, a multi-objective optimization model for power coordination of grid-type energy storage is constructed, with the reactive power droop coefficient adjustment of each grid-type energy storage converter as the optimization variable and the objectives of minimizing transient voltage deviation and maximizing reactive power capacity utilization. The multi-objective optimization model for power coordination of the grid-type energy storage is solved offline using a multi-objective optimization algorithm to generate an optimal reactive power droop coefficient adjustment mapping table covering typical operating conditions. The optimal reactive power droop coefficient adjustment amount is obtained by online table lookup, the reactive power droop coefficient is dynamically adjusted, and closed-loop feedback correction is performed.

2. The method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to claim 1, characterized in that: The selected microgrid operation data needs to be collected in real time to identify and detect transient characteristics of off-grid switching and to determine the microgrid operation status. include: Real-time acquisition of electrical operation data at the grid connection point via hard-wired connection, including: instantaneous values ​​of three-phase voltage, instantaneous values ​​of three-phase current, and system frequency at the grid connection point. f Active power P reactive power Q and grid-connected switch auxiliary contact position signal S grid ,in, S grid = 1 indicates a closed loop. S grid = 0 indicates disconnection; Based on the acquired electrical operation data of the grid connection point, the following transient characteristic quantities are calculated in real time using a sliding time window: voltage amplitude change rate Δ U / Δ t Rate of change of frequency df / dt Active power change rate Δ P / Δ t and reactive power change rate Δ Q / Δ t ; The acquired transient characteristic quantity is compared with a preset threshold, and simultaneously combined with the auxiliary contact position signal of the grid-connected switch. S grid The current value and historical value are used to determine the microgrid's operating state as grid-connected steady state, off-grid steady state, grid-connected to off-grid switching transient state, or off-grid to grid-connected switching transient state. Upon determining that a handover transient has been entered, the handover start time is immediately latched. t 0, and extract the start time of the switch. t Power surge peak Δ within the window before and after 0 P gap Total reactive power deficit Δ Q gap The impact direction is used as the constraint boundary input for the multi-objective optimization model.

3. The method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to claim 1, characterized in that: Based on the equivalent circuit model of a microgrid with grid-connected energy storage for grid-connected and off-grid switching, a collaborative reactive power control framework is constructed, including: voltage command conversion, reactive power allocation, and active-reactive power coordinated control. An equivalent circuit model of a microgrid with grid-type energy storage and off-grid switching is constructed. The equivalent circuit model treats the grid-type energy storage converter as a controlled voltage source and adopts a virtual synchronous generator control strategy. The active power control loop performs active power-frequency droop control, and the reactive power control loop performs reactive power-voltage droop control. Based on the established microgrid grid-connected / off-grid switching equivalent circuit model, a collaborative reactive power control framework is constructed. The voltage command conversion includes: generating a total reactive power support reference value based on the positive sequence voltage amplitude deviation at the grid connection point; the reactive power allocation includes: initial allocation based on the available reactive power capacity margin of each grid-type energy storage converter; and the active-reactive power coordinated control includes: updating the actual reactive power droop coefficient executed by each energy storage converter based on the reactive power droop coefficient adjustment amount output by the multi-objective optimization model.

4. A method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to any one of claims 1 to 3, characterized in that: The aforementioned multi-objective optimization model for power coordination of grid-based energy storage, based on a collaborative reactive power control framework, includes the following: The model uses the reactive power droop coefficient adjustment of each grid-type energy storage converter as the optimization variable, and aims to minimize transient voltage deviation and maximize reactive power capacity utilization. Using the reactive power droop coefficient adjustment amount of each grid-type energy storage converter as the optimization variable, the sum of the initial value of the reactive power droop coefficient based on the offline setting under rated operating conditions and the reactive power droop coefficient adjustment amount is taken as the actual reactive power droop coefficient. During the switching process t Voltage deviation Δ at time U ( t The integral over the duration of the switching process is the voltage deviation during the switching process, and thus a first objective function is constructed with the goal of minimizing the voltage deviation during the switching process. The ratio of the sum of the actual reactive power output of all grid-type energy storage converters to the maximum available reactive power capacity margin is the reactive power capacity utilization rate. A second objective function is constructed with the goal of maximizing the reactive power capacity utilization rate.

5. The method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to claim 4, characterized in that: Based on the control equations of the grid-type energy storage converter and the system power balance relationship, the switching process in the first objective function... t Voltage deviation Δ at time U ( t This is expressed as the reactive power droop coefficient adjustment amount Δ. D q,i The algebraic function is used to achieve optimization solutions.

6. The method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to claim 4, characterized in that: The actual reactive power output of each grid-type energy storage converter in the second objective function is expressed as the superposition of droop response component and active power coupling feedforward compensation component. The droop response component is generated based on the optimized reactive power droop coefficient, and the active power coupling feedforward compensation component is generated based on the feedforward compensation coefficient of grid-type energy storage converter i and the identified power impulse slope.

7. A method for coordinated control of grid-connected and off-grid-connected energy storage power in a microgrid based on coordinated reactive power control according to claim 5 or 6, characterized in that: Set constraints for the first objective function and the second objective function, including: boundary constraints for reactive power droop coefficient adjustment, reactive power capacity limit constraints, and lower limit constraints for total reactive power droop coefficient; The boundary constraint for the reactive power droop coefficient adjustment is that the adjustment amount of the reactive power droop coefficient of each grid-type energy storage converter is not lower than the lower limit of the adjustment amount and not higher than the upper limit of the adjustment amount. The reactive power limit is that the reactive power output or absorbed by each grid-type energy storage converter during the switching process does not exceed its available reactive power capacity margin. The lower limit constraint of the total reactive power droop factor is that the total reactive power droop factor shall not be lower than the total reactive power deficit or surplus and the allowable voltage deviation.

8. A method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to any one of claims 1 to 3, characterized in that: The multi-objective optimization model for power coordination of the grid-type energy storage, established by offline solution using a multi-objective optimization algorithm, generates an optimal reactive power droop coefficient adjustment mapping table covering typical operating conditions, including: Transient features are selected as the working condition index dimension and discretized within their expected range of change. An improved NSGA-II algorithm was used as the multi-objective optimization algorithm to solve the established multi-objective optimization model for power coordination of grid-type energy storage converters. The index values ​​of all discrete operating points and their corresponding optimal reactive power droop coefficient adjustment values ​​are organized into a multidimensional lookup table.

9. A method for coordinated control of grid-connected and off-grid-connected energy storage power in microgrids based on coordinated reactive power control according to any one of claims 1 to 3, characterized in that: The process of obtaining the optimal reactive power droop coefficient adjustment amount through online table lookup, dynamically adjusting the reactive power droop coefficient, and performing closed-loop feedback correction includes: Read the currently calculated transient characteristics: total reactive power deficit, power surge peak, and switching start time. t 0 voltage deviation Δ U ( t 0) and statistical values ​​of available reactive power capacity margin for each grid-type energy storage converter; quickly calculate the reactive power droop coefficient adjustment amount corresponding to each grid-type energy storage converter; After being constrained by boundary limits, the reactive power droop coefficient is updated as the final adjustment amount. After the parameters are updated, the controller continuously monitors the voltage amplitude at the grid connection point and compares it with the expected response. When the set conditions are met, the controller performs parameter fine-tuning in finite steps. When entering the target steady-state operation mode, the current optimized reactive power droop coefficient is locked as the new steady-state reference value, the table lookup and feedback correction calculations are stopped, the switching optimization mode is exited, and the normal steady-state droop control is restored.

10. A coordinated control system for grid-connected and off-grid-connected energy storage power in a microgrid based on coordinated reactive power control, operating the coordinated control method for grid-connected and off-grid-connected energy storage power in a microgrid according to claims 1 to 9, characterized in that, include: The module includes a data acquisition and identification module, a collaborative reactive power control framework module, a solver, a memory, and an output module. The acquisition and identification module is used to acquire microgrid operating status data and identify transient characteristics in real time. The collaborative reactive power control framework module is used to generate a total reactive power support benchmark value based on the grid connection point voltage deviation, perform initial allocation according to the reactive power margin of each energy storage unit, receive the reactive power droop coefficient adjustment amount output by the multi-objective optimization model, and update the reactive power droop coefficient actually executed by each energy storage unit. The solver is used to solve the multi-objective optimization model for power coordination of grid-type energy storage, and to generate the reactive power droop coefficient adjustment amount. The memory is used to store a multidimensional lookup table organized by the index values ​​of all discrete operating points and their corresponding optimal adjustment values. The output module is used to obtain the optimal adjustment amount by looking up a table online after the acquisition and identification module identifies that the system has entered a switching transient state and extracts the transient characteristic quantity, dynamically adjust the reactive power droop coefficient and perform closed-loop feedback correction.

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