Self-adaptive adjustment control method and system for accessing energy storage power station to power system

Abstract

The invention discloses an adaptive adjustment control method and system for accessing an energy storage power station to a power system, and belongs to the technical field of power grid control, and the method comprises the steps: obtaining a power grid node parameter, an energy storage power station operation parameter and an initial droop coefficient in the power system, constructing a power system distribution objective function, and combining a power system constraint condition to obtain a power system distribution objective function; carrying out optimization calculation to obtain peak regulation capacity, frequency modulation capacity and voltage regulation capacity distribution schemes of each energy storage power station; calculating a dynamic droop coefficient of each energy storage power station by combining the maximum fluctuation interval of the node voltage and the maximum fluctuation interval of the frequency of the power grid, and controlling each energy storage power station to adjust the output power according to the corresponding dynamic droop coefficient; in a set scheduling period, power grid node parameters and energy storage power station operation parameters are periodically updated, and then an adjustment capacity distribution scheme and a dynamic droop coefficient of each energy storage power station are updated and calculated. And adjusting the droop coefficient of the power station and accurately regulating the output of the power station according to the actual condition of the power station and node frequency and voltage regulation requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid control, in particular to a self-adaptive adjustment control method and system for connecting energy storage power stations to a power system. BACKGROUND

[0002] With the large-scale integration of renewable energy and the increasing demand for flexible resources in power systems, it is expected that large-scale energy storage power stations of hundreds of megawatts or even gigawatts will be increasingly used in the future. At present, with the significant increase in the number of energy storage units and battery cells in energy storage power stations, challenges are brought to the energy management of the power stations. The dispatching strategy of the power station needs to take into account both the steady-state peak clipping and valley filling and the transient peak shaving and voltage regulation. In terms of steady-state dispatching of energy storage power stations, the energy storage power stations need to be planned and coordinated to fully absorb new energy and achieve peak clipping and valley filling; in terms of transient dispatching of energy storage power stations, the energy storage power stations need to provide fast and accurate active and reactive power support to respond to power grid disturbances.

[0003] With the increasing demand of power grid for energy storage power stations, the traditional fixed droop coefficient strategy cannot meet the dynamic demand of power grid, affecting the transient stability and new energy consumption. SUMMARY

[0004] Based on the traditional droop characteristics of energy storage power stations, the present application proposes a self-adaptive adjustment control method for connecting energy storage power stations to a power system. The droop coefficient of the power station is adjusted to accurately control the output of the power station in combination with the actual situation of the power station and the node frequency and voltage regulation demand.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is as follows: In a first aspect, the present application provides a self-adaptive adjustment control method for connecting energy storage power stations to a power system, comprising: obtaining grid node parameters, energy storage power station operating parameters and initial droop coefficients in the power system; With the optimal power quality and the best peak shaving effect of the whole network as the target, a power system distribution objective function is constructed, and in combination with the power system constraint conditions, the peak shaving capacity, frequency modulation capacity and voltage regulation capacity of each energy storage power station are taken as decision variables for optimization calculation based on the grid node parameters, energy storage power station operating parameters and initial droop coefficients in the power system, to obtain the peak shaving capacity, frequency modulation capacity and voltage regulation capacity distribution scheme of each energy storage power station; the constraint conditions of the power system distribution objective function at least include power flow constraints, energy storage power station operating characteristic constraints and upper and lower limits of power quality constraints; Based on the peak shaving capacity, frequency modulation capacity and voltage regulation capacity distribution scheme of each energy storage power station, in combination with the maximum voltage fluctuation range and the maximum frequency fluctuation range of the grid node, the dynamic droop coefficients of each energy storage power station are calculated, including dynamic Q-V droop coefficients and P-f droop coefficients, and each energy storage power station is controlled to adjust the output power according to the corresponding dynamic droop coefficients.

[0006] As a further improvement of the application, the grid node parameters at least include node voltage, frequency, and corresponding standard value and fluctuation interval, the energy storage power station operation parameters at least include maximum apparent power, maximum active power, maximum reactive power, state of charge and health state of each power station, and the initial droop coefficient includes initial Q-V droop coefficient and initial P-f droop coefficient.

[0007] As a further improvement of the application, the grid node parameters, energy storage power station operation parameters and initial droop coefficient in the power system are obtained by: The state of charge and health state of the energy storage power station are collected by the battery management system, the real-time voltage and frequency of the grid node are collected by the grid monitoring system, and the voltage and frequency of the grid node at each time in the dispatching period are estimated in combination with historical operation data; the initial Q-V droop coefficient and initial P-f droop coefficient of the energy storage power station are collected by the energy management system.

[0008] As a further improvement of the application, the total number of dispatching periods is set to T, and the time interval of adjacent dispatching periods is set to Δt. The droop coefficient of the energy storage power station is adjusted once in each dispatching period.

[0009] As a further improvement of the application, the expression of the power system distribution target function is:

[0010] In the formula: is the power at the th node at the th time; is the average power in the dispatching interval of the th node; is the frequency of electric energy at the th node at the th time; is the standard value of electric energy frequency; is the voltage value at the th node at the th time; , , are weight coefficients of the three, respectively, and α1+α2+α3=1; The energy storage power station operation characteristic constraint includes that the output power of the energy storage power station does not exceed the power limit value corresponding to its maximum apparent power, the maximum active power does not exceed the upper limit of the peak regulation capacity, the maximum reactive power does not exceed the upper limit of the voltage support capacity, and the state of charge of the energy storage power station is in the set safe operation interval.

[0011] As a further improvement to this application, the dynamic droop coefficient of each energy storage power station is calculated based on the allocation scheme of peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage power station, combined with the maximum voltage fluctuation range and the maximum frequency fluctuation range of the power grid nodes. The dynamic droop coefficient includes the dynamic QV droop coefficient and the Pf droop coefficient, which include: At that moment, the Frequency regulation capacity allocation for each energy storage power station is as follows: The corresponding maximum active power output is Voltage regulation capacity allocation is as follows: The corresponding maximum active power output is . At time i, the droop coefficient of the i-th power station is calculated by the following formula.

[0012]

[0013] In the formula: For the maximum fluctuation range of node voltage, This represents the range of maximum fluctuation in node frequency. This represents the maximum reactive power output of the energy storage power station's voltage regulation capacity. This represents the maximum output active power of the energy storage power station's frequency regulation capacity.

[0014] Save the active power-frequency droop coefficient matrix of the power plant Reactive power-voltage droop coefficient matrix

[0015] ;

[0016] exist During the period, the first Each energy storage power station is based on the dynamic reactive power-voltage droop coefficient. Dynamic active power-frequency droop coefficient Adjust the power plant's output power.

[0017] As a further improvement to this application, it also includes: periodically updating the grid node parameters and energy storage power station operating parameters within a set scheduling cycle, thereby updating and calculating the regulation capacity allocation scheme and dynamic droop coefficient of each energy storage power station.

[0018] As a further improvement to this application, the step of periodically updating the grid node parameters and energy storage power station operating parameters within a set scheduling cycle, and then updating and calculating the regulation capacity allocation scheme and dynamic droop coefficient of each energy storage power station, specifically includes: When the power system runs to the k moment, k is a positive integer greater than the initial moment t and less than the total number of scheduling periods T, the state of charge is updated based on the charging and discharging power integration of the energy storage power station in the [t, k] period, the health state is updated based on the cycle number and the charging and discharging depth of the energy storage power station in the [t, k] period, and the real-time voltage and frequency of the power grid node and the corresponding estimated value are updated, and then the update operation of the regulation capacity allocation and the droop coefficient adjustment is performed.

[0019] In a second aspect, the application provides an adaptive regulation control system for an energy storage power station accessing a power system, based on the adaptive regulation control method for the energy storage power station accessing the power system, comprising: A data acquisition module is configured to acquire power grid node parameters, energy storage power station operating parameters and initial droop coefficients in the power system, wherein the power grid node parameters at least include node voltages, frequencies and corresponding standard values and fluctuation intervals, the energy storage power station operating parameters at least include maximum apparent power, maximum active power, maximum reactive power, state of charge and health state of each power station, and the initial droop coefficients include initial Q-V droop coefficients and initial P-f droop coefficients. A capacity allocation module is configured to construct a power system allocation objective function with the optimal power quality and the best peak shaving effect as the target, combine power system constraint conditions, and based on the power grid node parameters, the energy storage power station operating parameters and the initial droop coefficients in the power system, optimize calculation of the peak shaving capacity, the frequency modulation capacity and the voltage regulation capacity of each energy storage power station as decision variables to obtain a peak shaving capacity, a frequency modulation capacity and a voltage regulation capacity allocation scheme of each energy storage power station; the constraint conditions of the power system allocation objective function at least include power flow constraints, energy storage power station operating characteristic constraints, and upper and lower limits of power quality constraints. A coefficient adjustment module is configured to calculate dynamic droop coefficients of each energy storage power station based on the peak shaving capacity, the frequency modulation capacity and the voltage regulation capacity allocation scheme of each energy storage power station, combine the maximum voltage fluctuation interval and the maximum frequency fluctuation interval of the power grid node, and control each energy storage power station to adjust the output power according to the corresponding dynamic droop coefficients; the dynamic droop coefficients include dynamic Q-V droop coefficients and P-f droop coefficients. A cycle control module is configured to periodically update the power grid node parameters and the energy storage power station operating parameters in a set scheduling period, and then update the regulation capacity allocation scheme and the dynamic droop coefficients of each energy storage power station.

[0020] In a third aspect, the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the adaptive regulation control method for the energy storage power station accessing the power system when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the adaptive adjustment control method for connecting the energy storage power station to the power system.

[0022] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions instruct a computer to execute the adaptive adjustment control method for connecting the energy storage power station to the power system.

[0023] The present application has the following beneficial effects relative to the prior art: The present application measures the SOC and SOH states of the energy storage power station, calculates the voltages and frequencies of each node of the power grid, calculates the peak regulation capacity, frequency regulation capacity and voltage regulation capacity of each power station, adjusts the Q-V droop coefficient and P-f droop coefficient of the energy storage power station, and finally updates the data of the energy storage system and adjusts the distribution of the peak regulation capacity, frequency regulation capacity and voltage regulation capacity of the power station. By proposing adaptive Q-V droop coefficient and P-f droop coefficient, the potential value of the energy storage power station in supporting the transient voltage of the power grid is fully tapped while the peak regulation function of the energy storage power station is taken into account, so that the transient stability of the system is realized and the consumption level of new energy is improved. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present application or the prior art. It should be understood that the drawings in the following introduction are only for the convenience of clearly describing some embodiments of the technical solutions of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0025] Figure 1 The equivalent circuit for connecting the energy storage power station to the power system; Figure 2 The flow chart of the adaptive adjustment control method for connecting the energy storage power station to the power system; Figure 3 The adaptive adjustment control method and system for connecting the energy storage power station to the power system. DETAILED DESCRIPTION

[0026] The embodiments of the present application are described below in detail, examples of which are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only, for the purpose of explanation, and are not to be understood as a limitation of the present application. For the step numbers in the following embodiments, they are only set for the convenience of setting out the description, and the order between the steps is not limited in any way, and the execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0027] In the description of the present application, the words such as setting, installing, connecting and the like should be understood in a broad sense unless otherwise explicitly limited, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0028] In order to meet the demand of large-scale energy storage power station for optimizing management of mass batteries, the present application proposes a dynamic Q-V droop coefficient and P-f droop coefficient adjustment method, which adjusts the capacity allocation strategy of energy storage power station in the power grid by real-time analysis of the operation state of the energy storage system and the operation demand of the power grid. Based on the capacity allocation strategy of the power station, the two droop coefficients are adaptively adjusted to fully utilize the active and reactive capacity of the energy storage system, and the steady-state process peak regulation and consumption demand of the energy storage power station and the transient process frequency and voltage regulation demand are considered.

[0029] Therefore, the first object of the present application is to propose an adaptive adjustment control method for connecting energy storage power station to power system, comprising the following steps: Step 1, data acquisition and processing: obtaining power grid node parameters, energy storage power station operation parameters and initial droop coefficients in the power system, the power grid node parameters at least including node voltage, frequency and corresponding standard value and fluctuation interval, the energy storage power station operation parameters at least including maximum apparent power, maximum active power, maximum reactive power, state of charge (SOC) and state of health (SOH) of each power station, and the initial step 2, the droop coefficient includes initial Q-V droop coefficient and initial P-f droop coefficient; Step 2, adjusting capacity allocation: taking the optimal power quality and the best peak regulation effect of the whole network as the target, combining the power system constraint condition, based on the power grid node parameters, the energy storage power station operation parameters and the initial droop coefficients in the power system, taking the peak regulation capacity, the frequency regulation capacity and the voltage regulation capacity of each energy storage power station as the decision variable to optimize calculation, and obtaining the peak regulation capacity, the frequency regulation capacity and the voltage regulation capacity distribution scheme of each energy storage power station; the power system constraint condition at least includes power flow constraint, energy storage power station operation characteristic constraint and power quality upper and lower limit constraint; Step 3, dynamic adjustment of droop coefficient: based on the adjustment capacity allocation scheme obtained in step 2), combined with the maximum voltage fluctuation range of the grid node and the maximum frequency fluctuation range, the dynamic Q-V droop coefficient and the dynamic P-f droop coefficient of each energy storage power station are calculated, and each energy storage power station is controlled to adjust the output power according to the corresponding dynamic droop coefficient; Step 4, parameter updating and cyclic adjustment: within the set scheduling period, the grid node parameters and energy storage power station operating parameters are updated regularly, and steps 2-3 are repeated to update the adjustment capacity allocation scheme and dynamic droop coefficient of each energy storage power station, thereby realizing the continuous adaptive adjustment of the energy storage power station to the power system.

[0030] The following will be combined Figure 1 , Figure 1 The equivalent circuit of the energy storage power station connected to the power system is shown in the following figure: Step 1, measure the SOC and SOH states of the energy storage power station, and calculate the voltage and frequency of each node in the power grid.

[0031] The above scheme is specifically to collect the state of charge and health of the energy storage power station through the battery management system, collect the real-time voltage and frequency of the grid node through the grid monitoring system, and estimate the voltage and frequency of the grid node at each time within the scheduling period combined with historical operation data; collect the initial Q-V droop coefficient and initial P-f droop coefficient of the energy storage power station through the energy management system.

[0032] The total number of scheduling periods is set to T, and the time interval of adjacent scheduling periods is set to Δt. The droop coefficient of the energy storage power station is adjusted once within each scheduling period. For example, the total number of grid nodes is , the total number of energy storage power stations is This method adjusts the droop control coefficient of the power station every 15 minutes, and divides 96 scheduling periods in a day, i.e. =96; The maximum apparent power of the th power station is , the upper limit of the peak shaving capacity is the maximum active power , and the upper limit of the voltage support capacity is the maximum reactive power . They are stored as matrices , P, and Q, respectively, as boundary conditions for power station power allocation and droop coefficient calculation.

[0033] ; ;

[0034] For example, the maximum apparent power of the 1st energy storage power station is 200 MVA (i.e. =200 MVA); the maximum active power is 200 MW (i.e. =200MW); the maximum reactive power is 200Var (i.e., ... =200MW).

[0035] The battery management system (BMS) is used to measure the state signals of the energy storage power station, including SOC and SOH. At that moment, the Energy storage power station Status is , Status is As boundary conditions for power distribution and droop coefficient calculation in power plants, it is stored as a matrix. , .

[0036] ;

[0037] For example, at time t=0, the SOC of energy storage power station No. 1 is 80% (i.e., =0.8), SOH is 99% (i.e. =0.99).

[0038] Voltmeters and frequency meters are installed at each power grid node to collect power grid operation information, and data is collected in real time through the power grid monitoring system; Nodes The voltage value at time is Frequency value Simultaneously, based on historical information, the voltage and frequency values ​​of the power grid nodes between time t and the end of the scheduling period T are estimated and stored as a matrix. , .

[0039] ;

[0040] For example, at t=0, the voltage of node 1 in the power grid is 35kV (i.e., =35kV), with a frequency of 50.02Hz (i.e. =50.02Hz). It is estimated that at t=96, the voltage at grid node n is 35.1kV (i.e., 50.02kV). =35kV), with a frequency of 49.9Hz (i.e. =49.9Hz).

[0041] Measuring the operating signals of an energy storage power station using an Energy Management System (EMS) includes the QV droop coefficient. Pf droop coefficient

[0042] The sampling frequency for the battery management system (BMS) to collect SOC and SOH data shall not be less than once per minute, and the sampling frequency for the power grid monitoring system to collect node voltage and frequency data shall not be less than once per second.

[0043] Step 2: Allocate the peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of the energy storage power station.

[0044] After obtaining relevant information about energy storage power stations and grid nodes through step 1, the peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of the power stations can be allocated. The power system allocation objective function is set to achieve the best power quality and peak-shaving effect across the entire network.

[0045]

[0046] In the formula: For the first Nodes Power at any moment; For the first Average power within the scheduling interval of each node; For the first Nodes The frequency of electrical energy at any given moment; This is the standard value for electrical energy frequency; For the first Nodes Voltage value at any given time; For the first Standard values ​​for node voltages; , , These are the weighting coefficients for the three factors, and α1+α2+α3=1.

[0047] The weighting coefficients α1, α2, and α3 in the power system allocation objective function are dynamically adjusted according to the power grid operation requirements. When the power grid frequency fluctuates significantly, the value of α2 is increased; when the power grid voltage fluctuates significantly, the value of α3 is increased; and when it is necessary to prioritize the peak-shaving effect, the value of α1 is increased.

[0048] The peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each power station are set as decision variables to optimize the power quality of the entire network at key time points.

[0049] The operational constraints of the energy storage power station include: the output power of the energy storage power station does not exceed the power limit corresponding to its maximum apparent power, the maximum active power does not exceed the upper limit of peak-shaving capacity, the maximum reactive power does not exceed the upper limit of voltage support capacity, and the state of charge of the energy storage power station is within the set safe operating range. A detailed analysis follows: The constraints to be considered include: power flow constraints, energy storage power station operating characteristic constraints, and upper and lower limits of power quality constraints.

[0050] The optimization software is used for calculation to obtain future power station peak shaving capacity , frequency modulation capacity , voltage regulation capacity , and distribution scheme.

[0051] ; ;

[0052] Step 3, adjusting the energy storage power station Q-V droop coefficient and P-f droop coefficient. According to the power station capacity distribution scheme in step 2, the power station droop coefficient is adjusted.

[0053] The traditional reactive power-voltage droop coefficient and active power-frequency droop coefficient are fixed values, and the actual state of the power station and the node regulation demand are not considered. The improved reactive power-voltage droop coefficient and active power-frequency droop coefficient are calculated according to the power station capacity distribution scheme, so as to ensure the regulation effect of the power station and fully utilize the capacity of the power station.

[0054] At time t, the frequency modulation capacity of the i-th energy storage power station is allocated as , and the corresponding maximum active output is ; the voltage regulation capacity is allocated as , and the corresponding maximum active output is . . At time t, the droop coefficient of the i-th power station is calculated by the following formula

[0055]

[0056] In the formula: is the maximum fluctuation range of the node voltage, is the maximum fluctuation range of the node frequency; is the maximum output reactive power of the energy storage power station voltage regulation capacity, is the maximum output active power of the energy storage power station frequency modulation capacity.

[0057] Wherein, the maximum fluctuation range of the node voltage and the maximum fluctuation range of the node frequency f max According to the grid operation standard and historical disturbance data, wherein the maximum fluctuation range of the frequency is taken as the frequency standard value f ref , and the fluctuation range is not more than ±0.2 Hz.

[0058] The active power-frequency droop coefficient matrix of the power station is saved , and the reactive power-voltage droop coefficient matrix is saved .

[0059] ;

[0060] In the period, the first energy storage power station adjusts the output power according to the dynamic reactive-voltage droop coefficient , the dynamic active-frequency droop coefficient .

[0061] Step 4, update the energy storage system data, adjust the peak shaving capacity, frequency modulation capacity, and voltage modulation capacity distribution of the power station.

[0062] When the power system runs to the k moment, k is a positive integer greater than the initial moment t and less than the total number of scheduling periods T, the state of charge is updated based on the charging and discharging power integration of the energy storage power station in the [t, k] period, the health state is updated based on the cycle number and the charging and discharging depth of the energy storage power station in the [t, k] period, and the real-time voltage and frequency of the power grid node and the corresponding estimated value are updated, and then the update operation of the adjustment capacity distribution and the droop coefficient is performed.

[0063] The power station runs to the k moment, the energy storage power station data and the voltage and frequency estimation data of each node of the power grid are updated. The peak shaving capacity, frequency modulation capacity, and voltage modulation capacity of each energy storage power station are distributed again. The power system distribution target function is the best power quality and the best peak shaving effect of the whole network from the k moment to the end of scheduling

[0064] Update the future power station peak shaving capacity , frequency modulation capacity , and voltage modulation capacity distribution scheme Then update the active-frequency droop coefficient matrix , and the reactive-voltage droop coefficient matrix of the power station according to step three.

[0065] Specifically, according to the peak shaving capacity , frequency modulation capacity , and voltage modulation capacity distribution results of the energy storage power station, and combining the actual operating state of the power station, the reactive-voltage droop coefficient and the active-frequency droop coefficient of the power station are adjusted.

[0066] The value of the maximum fluctuation range U_max of the voltage of the power grid node is ±5% V_(n,ref), and the value of the maximum fluctuation range f_max of the frequency of the power grid node is ±0.5 Hz; V_(n,ref) is the standard voltage of the nth power grid node.

[0067] When the power system runs to the k moment (k is a positive integer greater than the initial moment t and less than the total number of scheduling periods T), the state of charge (SOC) is updated based on the charging and discharging power integration of the energy storage power station in the [t, k] period, the state of health (SOH) is updated based on the cycle number and the charging and discharging depth of the energy storage power station in the [t, k] period, and the real-time voltage and frequency of the power grid node and the corresponding estimated value are updated, and then the update operation of the regulation capacity allocation and the droop coefficient adjustment is performed.

[0068] Further, when updating the voltage and frequency estimation data of each node of the power grid, the historical estimation model is corrected in combination with the real-time collected actual values of the node voltage and frequency from the t moment to the k moment, so as to improve the accuracy of the voltage and frequency estimation value from the k moment to the end of the scheduling T.

[0069] The optimization calculation is realized by an optimization software, the optimization software is constructed based on a linear programming algorithm, a nonlinear programming algorithm or an intelligent optimization algorithm, and the intelligent optimization algorithm includes at least one of a particle swarm optimization algorithm and a genetic algorithm.

[0070] Further, the value of Δt is matched with the scheduling period, that is, Δt=15 minutes, so as to ensure that the energy storage power station adjusts the output power according to the current dynamic droop coefficient in one scheduling period.

[0071] With the expansion of the energy storage power station (hundred megawatt / gigawatt level), the number of energy storage units and battery monomers increases sharply, and the traditional scheduling cannot take into account the steady-state peak clipping and valley filling and the transient peak shaving and voltage regulation. Through the matrix management of the power station state (SOC, SOH matrix), the power grid parameter (V, F matrix) and the capacity allocation (Sᵢ, peck, Sᵢ, f, Sᵢ, v), in combination with the scheduling period (T=96) once every 15 minutes, the fine and systematic optimization management of the massive batteries is realized, and the scheduling confusion or resource waste is avoided.

[0072] The second object of the present application is to provide an adaptive regulation control system of an energy storage power station connected to a power system, based on the adaptive regulation control method of the energy storage power station connected to the power system, the system comprises: A data acquisition module is configured to acquire power grid node parameters, energy storage power station operation parameters and initial droop coefficients in the power system, wherein the power grid node parameters at least include node voltage, frequency and corresponding standard values and fluctuation intervals, the energy storage power station operation parameters at least include maximum apparent power, maximum active power, maximum reactive power, state of charge and state of health of each power station, and the initial droop coefficients include initial Q-V droop coefficients and initial P-f droop coefficients. The capacity allocation module is configured to construct a power system allocation objective function with the optimal power quality of the whole network and the optimal peak shaving effect as the target, combine power system constraints, and based on the grid node parameters, the energy storage power station operation parameters and the initial droop coefficient in the power system, optimize calculation of the peak shaving capacity, the frequency modulation capacity and the voltage regulation capacity of each energy storage power station as the decision variable to obtain the peak shaving capacity, the frequency modulation capacity and the voltage regulation capacity allocation scheme of each energy storage power station; the constraints of the power system allocation objective function at least include power flow constraints, energy storage power station operation characteristic constraints, and upper and lower limits of power quality constraints. The coefficient adjustment module is configured to calculate the dynamic droop coefficient of each energy storage power station based on the peak shaving capacity, the frequency modulation capacity and the voltage regulation capacity allocation scheme of each energy storage power station, combine the maximum voltage fluctuation range of the grid node and the maximum frequency fluctuation range, and control each energy storage power station to adjust the output power according to the corresponding dynamic droop coefficient, wherein the dynamic droop coefficient includes dynamic Q-V droop coefficient and P-f droop coefficient. The cycle control module is configured to periodically update the grid node parameters and the energy storage power station operation parameters within a set scheduling period, and then update the regulation capacity allocation scheme and the dynamic droop coefficient of each energy storage power station.

[0073] The application takes the optimal power quality of the whole network + the best peak shaving effect as the power system allocation objective function, takes the peak shaving capacity (Sᵢ,peck) as the core decision variable, and optimizes the allocation in combination with the power flow constraints and the power quality constraints. This design can plan the active output of the energy storage power station, fully utilize new energy generation, suppress the load fluctuation of the power grid, and meet the core demand of steady-state peak shaving. For the transient voltage and frequency regulation demand during power grid disturbance, the application adjusts the Q-V and P-f droop coefficients dynamically, so that the energy storage power station can quickly output reactive power (to cope with voltage fluctuation) and active power (to cope with frequency fluctuation). Compared with the traditional strategy, this method can accurately match the node frequency and voltage regulation demand, improve the transient stability of the power grid, and avoid power grid failure caused by transient power imbalance.

[0074] In the capacity allocation and coefficient adjustment, the maximum apparent power (S i ), the maximum active / reactive power (P i , t , Q i , t ) are taken as boundary conditions, and the SOC and SOH states are dynamically optimized. This design can avoid idle or overload of the energy storage capacity, fully utilize the active and reactive capacity of the energy storage system, and maximize the economic and technical value of the energy storage power station.

[0075] Based on existing mature equipment (BMS collects SOC / SOH, EMS collects droop coefficient, and power grid monitoring system collects voltage / frequency), and through optimization software (such as MATLAB and GAMS) to realize capacity allocation calculation, without additional development of complex hardware, the engineering application threshold is reduced. At the same time, the 15-minute scheduling cycle and the cyclic update mechanism can adapt to the operation requirements of different scale power grids (node number N and power station number E), and have wide adaptability.

[0076] A third object of the embodiments of the present application is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the adaptive adjustment control method of the energy storage power station accessing the power system when executing the computer program. The electronic device further comprises a communication interface and a bus.

[0077] A fourth object of the embodiments of the present application is to provide a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the adaptive adjustment control method of the energy storage power station accessing the power system.

[0078] A fifth object of the embodiments of the present application is to provide a computer program product, which comprises computer instructions, and the computer instructions instruct a computer to execute the adaptive adjustment control method of the energy storage power station accessing the power system.

[0079] The computer program instructions can also be stored in a computer readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction means, which implements the function specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0080] The computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer implemented process, and the instructions executed on the computer or other programmable device provide steps for implementing the function specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0081] The present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, RAM, ROM, optical storage etc.) embodying computer readable program code.

[0082] The present application is described in terms of exemplary embodiments, devices (systems), and computer program products in flowcharts and / or block diagrams. It will be understood that each block of the flowchart and / or block diagrams, and / or combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing element or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagrams in the variations can illustrate a flow of the whole process or a part of the process. Although each flow or a plurality of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions, the computer program instructions are not limited to the flowchart and / or block diagrams. Figure 1 The flowchart and / or block diagrams in the variations can illustrate a flow of the whole process or a part of the process. Although each flow or a plurality of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions, the computer program instructions are not limited to the flowchart and / or block diagrams.

[0083] Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should be within the protection scope of the present application.

[0084] Finally, it should be noted that the above embodiments are merely used to illustrate, but not limit the technical solutions of the present application, and the ordinary skilled in the art should understand that: the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. An adaptive adjustment and control method for connecting an energy storage power station to a power system, characterized in that, Includes the following steps: Obtain grid node parameters, energy storage power station operating parameters, and initial droop coefficient in the power system; With the goal of achieving optimal power quality and peak-shaving effect across the entire power grid, a power system allocation objective function is constructed. Combining power system constraints with grid node parameters, energy storage station operating parameters, and initial droop coefficients, the peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage station are used as decision variables for optimization calculation, resulting in allocation schemes for the peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage station. The constraints of the power system allocation objective function include at least power flow constraints, energy storage station operating characteristic constraints, and upper and lower limits of power quality constraints. Based on the allocation schemes of peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage power station, and combined with the maximum voltage fluctuation range and the maximum frequency fluctuation range of the power grid nodes, the dynamic droop coefficient of each energy storage power station is calculated. The dynamic droop coefficient includes the dynamic QV droop coefficient and the Pf droop coefficient, and each energy storage power station is controlled to adjust its output power according to the corresponding dynamic droop coefficient.

2. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 1, characterized in that, The grid node parameters include at least the voltage, frequency, and corresponding standard values ​​and fluctuation ranges of each node. The energy storage power station operating parameters include at least the maximum apparent power, maximum active power, maximum reactive power, state of charge, and health status of each power station. The initial droop coefficient includes the initial QV droop coefficient and the initial Pf droop coefficient.

3. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 1, characterized in that, The acquisition of grid node parameters, energy storage power station operating parameters, and initial droop coefficients in the power system includes: The battery management system collects the state of charge and health status of the energy storage power station, the grid monitoring system collects the real-time voltage and frequency of the grid nodes, and combines historical operating data to estimate the grid node voltage and frequency at each moment within the scheduling cycle; the energy management system collects the initial QV droop coefficient and the initial Pf droop coefficient of the energy storage power station.

4. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 3, characterized in that, The total number of scheduling cycles is set as T, the time interval between adjacent scheduling cycles is set as Δt, and the droop coefficient of the energy storage power station is adjusted once in each scheduling cycle.

5. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 1, characterized in that, The expression for the objective function of the power system allocation is: In the formula: For the first Nodes Power at any moment; For the first Average power within the scheduling interval of each node; For the first Nodes The frequency of electrical energy at any given moment; This refers to the standard value for electrical energy frequency. For the first Nodes Voltage value at any given time; For the first Standard values ​​for node voltages; , , These are the weighting coefficients of the three factors, and α1+α2+α3=1; The constraints on the operating characteristics of the energy storage power station include: the output power of the energy storage power station does not exceed the power limit corresponding to its maximum apparent power, the maximum active power does not exceed the upper limit of peak shaving capacity, the maximum reactive power does not exceed the upper limit of voltage support capacity, and the state of charge of the energy storage power station is within the set safe operating range.

6. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 1, characterized in that, The allocation scheme for peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage power station, combined with the maximum voltage fluctuation range and maximum frequency fluctuation range of the power grid nodes, calculates the dynamic droop coefficient of each energy storage power station. The dynamic droop coefficient includes the dynamic QV droop coefficient and the Pf droop coefficient, including: At that moment, the Frequency regulation capacity allocation for each energy storage power station is as follows: The corresponding maximum active power output is Voltage regulation capacity allocation is as follows: The corresponding maximum active power output is ; At time i, the droop coefficient of the i-th power station is calculated as follows: In the formula: For the maximum fluctuation range of node voltage, This represents the range of maximum fluctuation in node frequency. This represents the maximum reactive power output of the energy storage power station's voltage regulation capacity. This represents the maximum active power output of the energy storage power station's frequency regulation capacity. Save the active power-frequency droop coefficient matrix of the power plant Reactive power-voltage droop coefficient matrix : ； exist During the period, the first Each energy storage power station is based on the dynamic reactive power-voltage droop coefficient. Dynamic active power-frequency droop coefficient Adjust the power plant's output power.

7. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 1, characterized in that, Also includes: Within the set scheduling cycle, the grid node parameters and energy storage power station operating parameters are updated periodically, thereby updating and calculating the regulation capacity allocation scheme and dynamic droop coefficient of each energy storage power station.

8. The adaptive adjustment and control method for connecting an energy storage power station to a power system according to claim 7, characterized in that, Within a set scheduling cycle, the grid node parameters and energy storage power station operating parameters are periodically updated, thereby updating and calculating the regulation capacity allocation scheme and dynamic droop coefficient of each energy storage power station. Specifically, this includes: When the power system reaches time k, where k is a positive integer greater than the initial time t and less than the total number of scheduling cycles T, the state of charge is updated based on the integral of the charging and discharging power of the energy storage station in the time period [t,k]. The health status is updated based on the number of cycles and the charging and discharging depth of the energy storage station in the time period [t,k]. The real-time voltage and frequency of the grid nodes and their corresponding estimated values ​​are also updated, and then the update operations of regulating capacity allocation and droop coefficient adjustment are performed.

9. An adaptive adjustment and control system for connecting an energy storage power station to a power system, based on the adaptive adjustment and control method for connecting an energy storage power station to a power system according to any one of claims 1 to 8, characterized in that, include: The data acquisition module is used to acquire grid node parameters, energy storage power station operating parameters, and initial droop coefficients in the power system. The capacity allocation module is used to construct a power system allocation objective function with the goal of achieving optimal power quality and peak-shaving effect across the entire network. Combining power system constraints with grid node parameters, energy storage station operating parameters, and initial droop coefficients, it optimizes the peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage station as decision variables to obtain the allocation scheme for each energy storage station's peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity. The constraints of the power system allocation objective function include at least power flow constraints, energy storage station operating characteristic constraints, and upper and lower limits of power quality constraints. The coefficient adjustment module is used to calculate the dynamic droop coefficient of each energy storage power station based on the allocation scheme of peak-shaving capacity, frequency regulation capacity, and voltage regulation capacity of each energy storage power station, combined with the maximum voltage fluctuation range and the maximum frequency fluctuation range of the power grid node. The dynamic droop coefficient includes the dynamic QV droop coefficient and the Pf droop coefficient, and controls each energy storage power station to adjust its output power according to the corresponding dynamic droop coefficient.

10. The adaptive adjustment and control system for connecting an energy storage power station to a power system according to claim 8, characterized in that, Also includes: The cyclic control module is used to periodically update the grid node parameters and energy storage power station operating parameters within a set scheduling cycle, and then update and calculate the regulation capacity allocation scheme and dynamic droop coefficient of each energy storage power station.

11. An electronic device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the adaptive regulation and control method for connecting an energy storage power station to a power system as described in any one of claims 1-8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the adaptive adjustment and control method for connecting an energy storage power station to a power system as described in any one of claims 1-8.

13. A computer program product, said computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computer to execute the adaptive adjustment and control method for connecting the energy storage power station to the power system as described in any one of claims 1-8.

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