Electric energy quality control method and system based on energy storage system

Abstract

The invention provides an electric energy quality control method and system based on an energy storage system, relates to the technical field of electric energy quality control, and aims to identify collaborative disturbance attributes during voltage sag and three-phase imbalance through dynamic distortion information, perform scheduling amplitude limiting compensation on the collaborative disturbance attributes, and improve the control accuracy of the electric energy quality. Obtaining a layered amplitude limiting inflection point when the electric energy in the energy storage system is intelligently scheduled in the load-storage balance constraint region; carrying out fusion correction on the circulation out-of-limit deviation, generating a dynamic peak regulation criterion when the power grid carries out energy storage transfer adjustment in multiple time scales, and further determining a regulation and control guidance strategy during electric energy balance regulation and control in the energy storage system according to the dynamic peak regulation criterion; and performing tolerance adaptation control on the power quality of the power distribution network in the composite disturbance scene according to the hierarchical amplitude limiting inflection point and the regulation and control guidance strategy. According to the invention, tolerance adaptation control can be carried out on the power quality of the power distribution network in a composite disturbance scene, so that the power quality management and control efficiency under the participation of an energy storage system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power quality control, and more particularly, to a power quality control method and system based on an energy storage system. BACKGROUND

[0002] Power quality control is one of the core technologies to ensure the safe and stable operation of a power system, aiming to maintain power parameters such as voltage, current and frequency within the allowable range through monitoring, analysis and control means, and to reduce the impact of various disturbances on power equipment and user power consumption. Traditional compensation devices can only deal with a single disturbance, while power quality control of the energy storage system can achieve composite adaptation, formulate tolerance control rules according to the disturbance superposition characteristics and time scale, ensure normal operation of the equipment, and avoid excessive regulation loss. Power quality control is widely used in new energy distribution networks and sensitive load power supply scenarios, and by improving the accuracy and flexibility of disturbance response, it meets the demand for high-quality power consumption, and is an important means to improve the resilience of modern power grids and optimize power quality.

[0003] However, existing technologies mainly design control strategies for single disturbances, do not fully identify the coordinated disturbance properties of voltage sag and three-phase imbalance, and do not construct a layered limiting mechanism combining load and storage balancing constraints for energy storage scheduling, while ignoring the impact of circulating current deviation in load and power output trend regulation, so that the regulation of the energy storage system under composite disturbance lacks accurate boundaries, resulting in insufficient adaptability of the power balance regulation strategy to actual disturbance scenarios, and making it difficult to meet the demand for high proportion of energy access scenarios in terms of power quality control accuracy and system stability. Therefore, how to perform tolerance adaptation control on the power quality of the distribution network under composite disturbance scenarios to improve the power quality management and control efficiency of the energy storage system is a problem faced by the industry. SUMMARY

[0004] The present application provides a power quality control method and system based on an energy storage system, which can perform tolerance adaptation control on the power quality of the distribution network under composite disturbance scenarios to improve the power quality management and control efficiency of the energy storage system.

[0005] In a first aspect, the present application provides a power quality control method based on an energy storage system, which comprises the following steps:

[0006] Obtaining dynamic distortion information of voltage and current of a distribution network in a power grid intelligent dispatching platform;

[0007] Identifying the coordinated disturbance properties of voltage sag and three-phase imbalance through the dynamic distortion information, scheduling and limiting compensation for the coordinated disturbance properties, and obtaining the layered limiting turning points of the intelligent scheduling of the energy in the energy storage system in the load and storage balancing constraint region;

[0008] Determine the ring current out-of-limit deviation of the state of charge adjustment based on the load and power output trend in the power grid, fuse and correct the ring current out-of-limit deviation, generate a dynamic peak regulation criterion for energy storage transfer adjustment in the power grid in multiple time scales, and then determine the regulation and control guide strategy for energy balance regulation in the energy storage system according to the dynamic peak regulation criterion.

[0009] According to the layered amplitude limiting inflection point and the regulation and control guide strategy, the power quality of the distribution network under the composite disturbance scenario is tolerance adaptive controlled.

[0010] In this embodiment, the dynamic distortion information includes the cooperative disturbance attribute of voltage sag and three-phase imbalance, which specifically includes:

[0011] According to the dynamic distortion information, the cooperative disturbance characteristics of voltage sag and three-phase imbalance are extracted.

[0012] Based on the cooperative disturbance characteristics, the disturbance bias mode of voltage sag and three-phase imbalance is determined.

[0013] According to the cooperative disturbance characteristics and the disturbance bias mode, the cooperative disturbance attribute of voltage sag and three-phase imbalance is identified.

[0014] In this embodiment, the layered amplitude limiting inflection point refers to the power regulation threshold point corresponding to different state of charge intervals when the energy storage system is intelligently dispatched in the load-storage balance constraint region.

[0015] In this embodiment, determining the ring current out-of-limit deviation of the state of charge adjustment based on the load and power output trend in the power grid specifically includes:

[0016] Obtain load change data and power output trend data in the power grid;

[0017] Based on the load change data and the power output trend data, determine the alienation ring current sequence when the state of charge is adjusted;

[0018] Extract the ring current out-of-limit deviation of the state of charge adjustment based on the load and power output trend in the power grid from the alienation ring current sequence.

[0019] In this embodiment, the power output trend refers to the law of power output power change over time.

[0020] In this embodiment, the energy storage transfer adjustment refers to the process of reallocating energy among different time periods and regions by changing the charge and discharge state and power of energy storage devices to balance supply and demand.

[0021] In this embodiment, the regulation and control guide strategy for energy balance regulation in the energy storage system determined by the dynamic peak regulation criterion specifically includes:

[0022] determine a control adaptation gradient of the electric energy balance regulation in the energy storage system according to the dynamic peak regulation criterion;

[0023] construct a steady-state distribution trajectory of the electric energy balance regulation in the energy storage system according to the control adaptation gradient;

[0024] determine a control guidance strategy of the electric energy balance regulation in the energy storage system according to the steady-state distribution trajectory.

[0025] In the embodiment, the electric energy balance regulation refers to a process of keeping the supply and demand of electric energy in the power grid balanced in time and space by adjusting the charging and discharging state and power of the energy storage system.

[0026] In the embodiment, the composite disturbance scenario refers to a running condition in which multiple power quality disturbances such as voltage sag and three-phase imbalance are superimposed in the power distribution grid.

[0027] In a second aspect, the application provides an electric energy quality control system based on an energy storage system, which is used to execute an electric energy quality control method based on an energy storage system, and the control system comprises:

[0028] an information acquisition module, configured to acquire dynamic distortion information of voltage and current of the power distribution grid in the power grid intelligent dispatching platform;

[0029] a dispatching compensation module, configured to identify a collaborative disturbance attribute of voltage sag and three-phase imbalance through the dynamic distortion information, perform dispatching and amplitude limiting compensation on the collaborative disturbance attribute, and obtain a hierarchical amplitude limiting inflection point of the electric energy in the energy storage system when the electric energy is intelligently dispatched in a load and storage balance constraint region;

[0030] a fusion correction module, configured to determine a circulating current overrun deviation when the state of charge is adjusted based on the load and electric energy output trend in the power grid, perform fusion correction on the circulating current overrun deviation, generate a dynamic peak regulation criterion of the power grid when the power grid is adjusted by energy storage transfer in multiple time scales, and then determine a control guidance strategy of the electric energy balance regulation in the energy storage system according to the dynamic peak regulation criterion;

[0031] an adaptation control module, configured to perform tolerance adaptation control on the power quality of the power distribution grid under the composite disturbance scenario according to the hierarchical amplitude limiting inflection point and the control guidance strategy.

[0032] The technical scheme provided by the embodiments of the application has the following beneficial effects:

[0033] The dynamic distortion information of the voltage and current of the distribution network in the power grid intelligent scheduling platform is acquired; the collaborative disturbance attribute of voltage sag and three-phase imbalance is identified through the dynamic distortion information, the collaborative disturbance attribute is compensated by scheduling limiting, the hierarchical limiting inflection point of the intelligent scheduling of the electric energy in the energy storage system in the load and storage balance constraint region is obtained, the ring current over-limit deviation of the state of charge adjustment based on the load and electric energy output trend in the power grid is determined, the ring current over-limit deviation is fused and corrected, the dynamic peak shaving criterion of the power grid in the energy storage transfer adjustment in multiple time scales is generated, and then the regulation and control guide strategy of the electric energy balance regulation and control in the energy storage system is determined; and the power quality of the distribution network under the composite disturbance scene is tolerance adaptive controlled according to the hierarchical limiting inflection point and the regulation and control guide strategy.

[0034] It can be seen that in the present application, the accuracy of power quality management and control of the distribution network can be improved under the premise that the existing power quality control based on the energy storage system has insufficient awareness of composite disturbance and single regulation and control strategy; wherein, by acquiring the dynamic distortion information of the voltage and current of the distribution network in the power grid intelligent scheduling platform, the abnormal characteristics of the voltage and current can be fully captured, complete and real-time data can be provided for collaborative disturbance analysis, the problem of traditional monitoring information fragmentation can be solved, and the perception ability of the abnormal state of power quality can be significantly enhanced. By identifying the collaborative disturbance attribute of voltage sag and three-phase imbalance and obtaining the hierarchical limiting inflection point, the limitation of single disturbance analysis can be broken through, the differentiated adjustment threshold can be formulated in combination with the energy storage load and storage balance constraint, the limitation of traditional fixed limiting can be broken, and the compensation accuracy and safety of the energy storage system under composite disturbance can be improved. By determining the ring current over-limit deviation, generating the dynamic peak shaving criterion and the regulation and control guide strategy, the ring current risk in the energy storage regulation can be corrected, the scheduling rules suitable for multiple time scales can be formed, the problem that the traditional peak shaving strategy is out of touch with the actual load and output trend can be avoided, and the stability of the energy storage balance regulation can be improved. By tolerance adaptive control according to the hierarchical limiting inflection point and the regulation and control guide strategy, dynamic adaptive regulation of power quality under the composite disturbance scene can be realized, the control accuracy and economy can be balanced, the problems of traditional control over-regulation or under-regulation can be solved, and the operation reliability of the distribution network under complex working conditions can be improved.

[0035] In summary, the technical solution adopted in the present application can perform tolerance adaptive control on the power quality of the distribution network under the composite disturbance scene to improve the power quality management and control efficiency of the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description only relate to the embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without any creative effort.

[0037] Figure 1 is an exemplary flow chart of a power quality control method based on an energy storage system according to the present application;

[0038] Figure 2 is a flow chart for determining a layered amplitude limiting inflection point according to the present application;

[0039] Figure 3 is a flow chart for determining a dynamic peak regulation criterion according to the present application;

[0040] Figure 4 is a module structure diagram of a power quality control system based on an energy storage system according to the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only relate to some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0042] The embodiments of the present application provide a power quality control method and system based on an energy storage system. The core is to obtain dynamic distortion information of voltage and current in a power distribution network in a power grid intelligent dispatching platform. The dynamic distortion information is used to identify the cooperative disturbance attribute of voltage sag and three-phase imbalance. The layered amplitude limiting inflection point of the energy in the energy storage system when the energy is intelligently dispatched in the load-storage balance constraint region is obtained by dispatching and limiting compensation of the cooperative disturbance attribute. The circulating current deviation beyond the limit when the state of charge is adjusted based on the load and energy output trend in the power grid is determined. The dynamic peak regulation criterion when the energy storage is transferred and adjusted in the power grid in multiple time scales is generated by fusion correction of the circulating current deviation beyond the limit. Then, the regulation and control guide strategy when the energy in the energy storage system is balanced and controlled is determined by the dynamic peak regulation criterion. The power quality of the power distribution network under the composite disturbance scene is tolerance adapted and controlled according to the layered amplitude limiting inflection point and the regulation and control guide strategy.

[0043] Embodiment one, in order to better understand the above technical solutions, the above technical solutions will be described in detail in the following with reference to the drawings in the specification and specific embodiments. Refer to Figure 1As shown, the figure is an example flow chart of a power quality control method based on an energy storage system according to the embodiment of the present application, the control method comprising the following steps:

[0044] In step S1, the dynamic distortion information of the voltage and current of the distribution network in the power grid intelligent dispatching platform is obtained.

[0045] In the specific implementation, first, high-precision synchronous phasor measurement devices and smart meters are deployed at the key nodes of the distribution network in the energy storage system, which can be the outlet end of the substation, the important load access point, and the distributed power grid connection point. The sampling frequency of these devices is not less than 2 kHz to ensure that high-frequency distortion signals can be captured. Then, the devices collect real-time voltage and current instantaneous value data, and transmit the data to the real-time database of the power grid intelligent dispatching platform through the power dedicated communication network. The power grid intelligent dispatching platform pre-processes the received data, including removing noise (using an adaptive Kalman filter algorithm to adjust the filter parameters by real-time estimation of noise variance to filter out random interference signals), data synchronization (according to the second pulse signal provided by the global positioning system, the sampling data of different nodes are calibrated to the same time axis, and the error is controlled within 10 microseconds), and finally forming a dynamic data set containing parameters such as voltage sag amplitude, duration, three-phase imbalance degree, and harmonic content. The data set is used as the dynamic distortion information of the voltage and current of the distribution network in the power grid intelligent dispatching platform.

[0046] It should be noted that in the present application, the dynamic distortion information refers to various dynamic characteristic data of voltage and current signals deviating from the normal sinusoidal waveform during the operation of the distribution network.

[0047] In step S2, the synergistic disturbance attribute of voltage sag and three-phase imbalance is identified through the dynamic distortion information, the synergistic disturbance attribute is dispatched and limited amplitude compensated, and the hierarchical limited amplitude inflection point of the energy in the energy storage system when intelligently dispatched in the load-storage balance constraint region is obtained.

[0048] In the present embodiment, the synergistic disturbance attribute of voltage sag and three-phase imbalance can be realized by the following steps:

[0049] According to the dynamic distortion information, the synergistic disturbance characteristics of voltage sag and three-phase imbalance are extracted;

[0050] Based on the synergistic disturbance characteristics, the disturbance bias mode of voltage sag and three-phase imbalance is determined;

[0051] According to the synergistic disturbance characteristics and the disturbance bias mode, the synergistic disturbance attribute of voltage sag and three-phase imbalance is identified.

[0052] In a specific implementation, first, the instantaneous values and duration of each phase voltage are extracted from the dynamic distortion information. For voltage sag, the sag depth (the difference between the actual and rated voltage divided by the rated value), the start and end time, and the fluctuation amplitude are calculated. For three-phase imbalance, the effective value of the negative sequence voltage component is decomposed using the symmetrical component method, and the phase sequence imbalance degree (the ratio of the negative sequence component to the positive sequence component) and the ratio of the maximum three-phase voltage difference to the rated voltage are calculated. These parameters are integrated to calculate the correlation value of the sag depth and the negative sequence component, and the change rate of the phase sequence imbalance degree and the sag duration. The calculated results are used as the coordinated disturbance characteristics representing the voltage sag and three-phase imbalance. Then, the coordinated disturbance characteristics are input into an analysis model trained based on historical data. The influence coefficient of the sag depth on the negative sequence component and the extension coefficient of the phase sequence imbalance degree on the sag duration are calculated, wherein the influence coefficient is determined by the ratio of the change amount of the sag depth to the change amount of the negative sequence component, and the extension coefficient is the increase amount of the sag duration when the phase sequence imbalance degree increases by 1%. If the influence coefficient is positive and has a large value, it is determined that the "voltage sag dominates the bias"; if the extension coefficient is positive and has a large value, it is determined that the "three-phase imbalance dominates the bias"; if the values of the influence coefficient and the extension coefficient are small and alternate in sign, it is determined that there is no obvious dominant bias, i.e., the disturbance bias mode of the voltage sag and three-phase imbalance is obtained. Finally, a multi-dimensional recognition framework is constructed, and the coordinated disturbance characteristics and the disturbance bias mode are combined. The superposition intensity index is calculated, which is the weighted sum of the normalized parameters, and the weights are determined according to the historical influence degree, reflecting the severity of the superposition. According to the bias mode, the influence range is determined: when the voltage sag dominates, the affected load area is determined according to the sag depth distribution; when the three-phase imbalance dominates, the affected nodes are determined according to the propagation characteristics of the phase sequence imbalance degree. By comprehensively considering the time parameters and the duration of the bias mode, the instantaneous, short-term or long-term superposition type is determined, and the specific coordinated disturbance attributes are recognized by integrating the information, i.e., the coordinated disturbance attributes of the voltage sag and three-phase imbalance are obtained.

[0053] It should be noted that in the present application, voltage sag refers to a power quality problem in which the voltage in a power system suddenly decreases and lasts for a certain period of time; three-phase imbalance refers to a state in which the amplitudes or phases of the voltages and currents in a three-phase power system are different; coordinated disturbance characteristics refer to a set of key parameters that can simultaneously reflect the superposition of voltage sag and three-phase imbalance; disturbance bias mode refers to the biasing rule of the influence of one side on the other side when voltage sag and three-phase imbalance are superimposed; and coordinated disturbance attributes refer to the overall characteristics exhibited when voltage sag and three-phase imbalance are superimposed.

[0054] Preferably, in the present embodiment, the coordinated disturbance attributes are scheduled, limited and compensated to obtain the hierarchical limited turning points of the intelligent scheduling of the electrical energy in the energy storage system in the load-storage balance constraint region. Figure 2As shown, the figure is a flow diagram of determining the layered clipping inflection point in some embodiments of the application. The layered clipping inflection point in the embodiment can be achieved by using the following steps:

[0055] In step S21, the disturbance influence index of each disturbance factor on the energy scheduling of the energy storage system is determined according to the cooperative disturbance attribute;

[0056] In step S22, the constraint clipping feature of the energy in the energy storage system under the charge and storage balance constraint is determined based on all the disturbance influence indexes;

[0057] In step S23, the clipping demand description of the energy in the energy storage system under the intelligent scheduling of the charge and storage balance constraint region is determined according to the constraint clipping feature;

[0058] In step S24, the layered clipping inflection point of the energy in the energy storage system under the intelligent scheduling of the charge and storage balance constraint region is determined according to the clipping demand description.

[0059] In a specific implementation, first, the voltage sag depth, duration, negative sequence component of three-phase imbalance, and phase sequence imbalance degree of the voltage sag are extracted from the coordinated disturbance attribute. For each factor, an impact evaluation dimension is set, such as the deviation of the energy storage charging and discharging power, and the reduction rate of the state of charge adjustment accuracy. Through historical operation data, an association model of each factor and the impact dimension is established, the actual value of each factor is substituted into the model, and the quantized value of each factor in different dimensions is calculated. Then, the disturbance influence index of each disturbance factor is obtained by weighted summation (the weight is set according to the actual influence degree of the factor on the scheduling). Next, all disturbance influence indexes are collected and classified according to the influence type, such as power fluctuation type and state of charge deviation type. For each type of index, the limiting threshold when the index exceeds the normal range is calculated in combination with the charge and storage balance constraint parameters of the energy storage system (such as the upper and lower limits of the state of charge and the maximum charging and discharging power). For example, when the power fluctuation type index exceeds the set value, the maximum allowed charging and discharging power adjustment amplitude is determined; when the state of charge deviation type index exceeds the standard, the emergency adjustment range of the state of charge is determined. Integrating these thresholds and ranges, the constraint limiting feature including the power limiting interval and the state of charge adjustment boundary is formed, that is, the constraint limiting feature of the electrical energy in the energy storage system under the charge and storage balance constraint. Then, the power limiting interval, state of charge adjustment boundary, and other contents in the constraint limiting feature are analyzed. For the power aspect, the allowed charging and discharging power range under different disturbance influences is described according to the limiting interval, such as the charging and discharging power can be between 60% and 80% of the rated power when the disturbance influence is small. For the state of charge aspect, the limiting requirement under different states is described according to the adjustment boundary, such as limiting the discharging power when the state of charge is below 30%. The classification results are used as the limiting requirement description of the electrical energy in the energy storage system under the charge and storage balance constraint region for intelligent scheduling. Finally, according to the limiting requirement description under different states of charge, the state of charge in the charge and storage balance constraint region is divided into three intervals: high, medium, and low. For each interval, the power adjustment threshold point in the interval is determined by simulation (simulating the effect of adjusting the power under different states of charge) in combination with the power range in the limiting requirement. For example, in the high state of charge interval (70% to 90%), the maximum discharging power threshold allowed according to the limiting requirement is determined as the lower inflection point of the interval; in the medium state of charge interval (30% to 70%), the upper and lower thresholds of the charging and discharging power are determined as the inflection points; in the low state of charge interval (10% to 30%), the maximum charging power threshold is determined as the upper inflection point, and finally the hierarchical limiting inflection points of the electrical energy in the energy storage system under the charge and storage balance constraint region for intelligent scheduling are obtained.

[0060] It should be noted that in the present application, the charge and storage balance constraint region refers to the operating range of the energy storage system in which the state of charge is maintained in the safe interval and the charging and discharging power does not exceed the limit; the disturbance influence index refers to a parameter quantifying the influence degree of each disturbance factor on the energy storage system electric energy scheduling process; the constraint limiting feature refers to the limiting characteristics of power, state of charge and the like that need to be followed in the energy storage system electric energy scheduling process under the charge and storage balance constraint; the limiting requirement description refers to the limiting conditions that need to be met when the energy storage system is intelligently scheduled within the charge and storage balance constraint region; and the hierarchical limiting inflection point refers to the power adjustment threshold point corresponding to different state of charge intervals when the energy storage system is intelligently scheduled within the charge and storage balance constraint region.

[0061] In step S3, the over-limit deviation of the circulating current when the state of charge is adjusted based on the load and the power output trend in the power grid is determined, the over-limit deviation of the circulating current is fused and corrected, the dynamic peak regulation criterion when the energy storage is transferred and adjusted in the power grid in multiple time scales is generated, and then the regulation and control guide strategy when the electric energy is balanced and controlled in the energy storage system is determined according to the dynamic peak regulation criterion.

[0062] In the present embodiment, the over-limit deviation of the circulating current when the state of charge is adjusted based on the load and the power output trend in the power grid can be achieved by the following steps:

[0063] Obtain the load change data and the power output trend data in the power grid;

[0064] Determine the alienation circulating current sequence when the state of charge is adjusted based on the load change data and the power output trend data;

[0065] Extract the over-limit deviation of the circulating current when the state of charge is adjusted based on the load and the power output trend in the power grid from the alienation circulating current sequence.

[0066] In a specific implementation, first, install intelligent monitoring terminals at each load node of the power grid, which can collect real-time power data of various loads such as industrial, residential, and commercial loads at 15-minute intervals to form load change data. Through the monitoring system of the new energy power station, real-time data of photovoltaic and wind power output are collected, combined with historical data and weather forecast information, and the time series analysis method is used to generate 24-hour power output trend data, which is not described here. Then, a state of charge adjustment model is constructed, and the load change data and power output trend data are input to calculate the required energy storage charging and discharging power at different times to maintain power balance of the power grid. Based on the topology of the power grid, the calculated charging and discharging power is substituted into the power grid model to solve the circulating current value at each time. By comparing the circulating current value with the reference circulating current value during normal operation (without state of charge adjustment), the circulating current deviation at each time is obtained, and the dissimilation circulating current sequence during state of charge adjustment is formed in chronological order. Finally, according to the parameters of the power grid equipment (such as the rated tolerance of cables and transformers), the circulating current safety limit is determined. The dissimilation circulating current sequence is traversed, and the circulating current value at each time is compared with the safety limit one by one to filter out all circulating current data exceeding the limit. For each over-limit data, the difference between the over-limit value and the limit value is calculated, and these differences are sorted in chronological order to form the circulating current over-limit deviation during state of charge adjustment based on load and power output trend in the power grid.

[0067] It should be noted that in this application, the power output trend refers to the law of power output power changing with time; the load change data refers to the quantitative information of various power loads in the power grid changing dynamically with time; the power output trend data refers to the information of the law of power output power changing with time; the dissimilation circulating current sequence refers to the sequence of circulating current deviating from the normal operating state in the power grid during the adjustment of the state of charge of the energy storage based on load and power output trend; and the circulating current over-limit deviation refers to the difference between the part exceeding the limit value of the safe operation of the power grid and the limit value.

[0068] Preferably, in this embodiment, the circulating current over-limit deviation is fused and corrected to generate a dynamic peak regulation criterion for energy storage transfer adjustment in multiple time scales of the power grid, as shown in Figure 3 The figure is a flowchart for determining the dynamic peak regulation criterion in some embodiments of the application. The dynamic peak regulation criterion in this embodiment can be realized by the following steps:

[0069] In step S31, the dynamic operation decision of the power grid running in multiple time scales is collected.

[0070] In step S32, the transfer delay rule during energy storage transfer adjustment is determined according to the circulating current over-limit deviation.

[0071] In step S33, the dynamic operation decision is mapped into the transfer delay rule to obtain an elastic peak regulation constraint of the power grid when the energy storage transfer adjustment is performed in multiple time scales;

[0072] In step S34, a dynamic peak regulation criterion of the power grid when the energy storage transfer adjustment is performed in multiple time scales is determined according to the elastic peak regulation constraint.

[0073] In a specific implementation, first, three time scales of second, minute and hour are divided, and decision data in each scale is extracted from a real-time database of a power grid dispatching intelligent dispatching system, including load distribution instructions, energy storage charging and discharging plans, power output adjustment schemes and the like. The data is time-stamped and uniformly formatted, and is stored in a classified manner according to the time scales to form dynamic operation decisions of the power grid in multiple time scales. Then, the loop flow overrun deviation is divided into three levels according to the size, i.e., slight: loop flow overrun deviation ≤ 10% limit value; moderate: loop flow overrun deviation 10% ~ 30%; severe: loop flow overrun deviation > 30%. For each level of deviation, a safe adjustment delay length is combined with historical data statistics, such as a slight deviation delay of 5 seconds, a moderate delay of 10 seconds and a severe delay of 15 seconds. The rules include that the delay time increases when the deviation level rises, and the delay is recalculated according to the current level when the deviation decreases. The formulated rules are used as transfer delay rules when the energy storage transfer adjustment is performed. Then, the dynamic operation decisions and the transfer delay rules are matched according to the time scales. The second-level decision corresponds to the severe deviation delay rule, and the limited single adjustment power is ≤ 20% of the rated power; the minute-level decision corresponds to the moderate deviation rule, and the adjustment power is allowed to be in the range of 20% ~ 50% with an interval ≥ 10 seconds; and the hour-level decision corresponds to the slight deviation rule, and the adjustment power can be in the range of 50% ~ 80% with a delay of 5 seconds. The power ranges and time intervals of the scales are integrated to form an elastic peak regulation constraint of the power grid when the energy storage transfer adjustment is performed in multiple time scales. Finally, trigger conditions of each time scale are extracted from the elastic peak regulation constraint. The second-level trigger condition is that the real-time power fluctuation ≥ 10% of the rated power and the loop flow deviation reaches the severe level, triggering the peak regulation; the minute-level trigger condition is that the average power deviation in 5 minutes ≥ 5% and the deviation is moderate, triggering the peak regulation; and the hour-level trigger condition is that the predicted load gap after 1 hour ≥ 8% and the deviation is slight, triggering the peak regulation. The conditions are bound with the corresponding adjustment power and delay time to form a dynamic peak regulation criterion of the power grid when the energy storage transfer adjustment is performed in multiple time scales.

[0074] It should be noted that in the present application, the energy storage transfer adjustment refers to the process of re-distributing electric energy between different time periods and regions by changing the charge and discharge state and power of the energy storage device to balance supply and demand; the dynamic operation decision refers to the real-time scheduling strategy formulated by the power grid at different time scales to maintain stable operation; the transfer delay rule refers to the delay time specification set for the energy storage transfer adjustment operation to avoid the expansion of loop flow beyond the limit; the elastic peak shaving constraint refers to the range and conditions allowed for energy storage transfer adjustment under multiple time scales; and the dynamic peak shaving criterion refers to the specific conditions and thresholds for starting energy storage transfer adjustment under multiple time scales.

[0075] In the present embodiment, the determination of the control guidance strategy for the electric energy balance regulation and control in the energy storage system according to the dynamic peak shaving criterion can be achieved by the following steps:

[0076] determining a control adaptation gradient for the electric energy balance regulation and control in the energy storage system according to the dynamic peak shaving criterion;

[0077] constructing a steady-state distribution trajectory for the electric energy balance regulation and control in the energy storage system according to the control adaptation gradient;

[0078] determining a control guidance strategy for the electric energy balance regulation and control in the energy storage system according to the steady-state distribution trajectory.

[0079] In a specific implementation, first, the time scale (second level, minute level, hour level) and trigger threshold (such as power fluctuation ratio, circulating current deviation level) in the dynamic peak regulation criterion are analyzed. The peak regulation emergency degree is divided into gradients: the second level criterion corresponds to the first gradient (high-intensity regulation), and the single power adjustment amplitude is set to 30% to 50% of the rated power; the minute level criterion corresponds to the second gradient (medium-intensity regulation), and the adjustment amplitude is 10% to 30%; the hour level criterion corresponds to the third gradient (low-intensity regulation), and the adjustment amplitude is 5% to 10%. For each gradient, the response time is matched, the response time of the first gradient is less than or equal to 1 second, the response time of the second gradient is less than or equal to 10 seconds, and the response time of the third gradient is less than or equal to 60 seconds, that is, the regulation adaptation gradient of the energy storage system is obtained. Then, based on the adjustment amplitude and response time of each regulation adaptation gradient, combined with the energy storage state of charge constraint (such as upper and lower limits of the state of charge), a segmented linear programming method is used to construct a trajectory. Under the first gradient, power nodes are set at 1 second intervals to ensure that the power difference between adjacent nodes is less than or equal to 10% of the rated power; nodes are set at 10 second intervals under the second gradient, and the power difference is less than or equal to 5%; nodes are set at 60 second intervals under the third gradient, and the power difference is less than or equal to 3%. The trajectory needs to cover the whole period from the triggering to the end of the peak regulation criterion, and the power change needs to be continuous and within the gradient allowed range, that is, the steady-state distribution trajectory of the energy storage system energy balance regulation is obtained. Finally, the steady-state distribution trajectory is decomposed into specific operation instructions at time nodes. Each node clearly indicates the energy storage charging and discharging state (charging / discharging / standby), real-time power value, and duration. For example, under the first gradient, a certain node instruction is "discharge, 30% rated power, and last for 1 second". Combined with the device operating parameters (such as the maximum switching frequency), state switching conditions (such as switching to charging when the state of charge drops to 20%) are added to the instructions. All node instructions are sorted by time to form a time-division, executable regulation guide strategy, that is, the regulation guide strategy for energy storage system energy balance regulation.

[0080] It should be noted that in this application, energy balance regulation refers to the process of balancing the supply and demand of electrical energy in the power grid in time and space by adjusting the charging and discharging state and power of the energy storage system; the regulation adaptation gradient refers to a sequence of levels matched with the intensity of energy storage regulation according to different dynamic peak regulation requirements; the steady-state distribution trajectory refers to the stable path of the charging and discharging power of the energy storage system changing with time under each regulation adaptation gradient; and the regulation guide strategy refers to a specific operation scheme for guiding the energy storage system to balance electrical energy.

[0081] In step S4, the power quality of the power distribution network under the composite disturbance scenario is tolerance-adapted controlled according to the layered limiting inflection point and the regulation guide strategy.

[0082] In a specific implementation, the tolerance adaptive control of power quality of the power distribution network under the composite disturbance scenario according to the hierarchical limiting inflection point and the regulation guidance strategy can be implemented in the following manner. First, the power quality tolerance range of the power distribution network under the composite disturbance scenario is set, including that the voltage sag depth is not more than 10%, the three-phase imbalance degree is not more than 2%, etc. The voltage, current, power and other operation data of the power distribution network are monitored in real time, and the state of charge and operation parameters of the energy storage system are obtained. The monitoring data are compared with the tolerance range, and when the disturbance occurs and is within the tolerance range, the allowed adjustment power range of the energy storage system is determined in combination with the hierarchical limiting inflection point, and the specific charging and discharging mode and adjustment rhythm are selected in combination with the regulation guidance strategy. For example, when it is detected that the voltage sag depth is 8% and the three-phase imbalance degree is 1.5%, if the energy storage is in the medium state of charge interval, discharging adjustment is performed in the range of 50% to 70% of the rated power according to the regulation guidance strategy. The disturbance change is continuously tracked, and the adjustment intensity is dynamically adjusted to ensure that the power quality operation parameters are stably within the tolerance range, thereby realizing the tolerance adaptive control of power quality. Details are not described herein.

[0083] It should be noted that in the present application, the composite disturbance scenario refers to the operation condition in which multiple power quality disturbances such as voltage sag and three-phase imbalance are superimposed in the power distribution network; and the tolerance adaptive control refers to a control mode in which the system is dynamically adjusted to adapt to the disturbance within the preset power quality tolerance range.

[0084] It can be seen that in the present application, the accuracy of power quality management and control of the power distribution network can be improved under the premise that the existing power quality control based on the energy storage system has insufficient awareness of composite disturbances and single regulation strategy. By obtaining the dynamic distortion information of the voltage and current of the power distribution network in the grid intelligent dispatching platform, the abnormal characteristics of the voltage and current can be fully captured, complete and real-time data can be provided for collaborative disturbance analysis, the problem of fragmented traditional monitoring information can be solved, and the perception ability of power quality abnormal state can be significantly enhanced. By identifying the collaborative disturbance attributes of voltage sag and three-phase imbalance and obtaining the hierarchical limiting inflection point, the limitation of single disturbance analysis can be broken through, the differentiated adjustment threshold can be formulated in combination with the energy storage charge and discharge balance constraint, the limitation of traditional fixed limiting can be broken, and the compensation accuracy and safety of the energy storage system under composite disturbance can be improved. By determining the loop flow overrun deviation, generating the dynamic peak regulation criterion and the regulation guidance strategy, the loop flow risk in energy storage adjustment can be corrected, the scheduling rules suitable for multiple time scales can be formed, the problem that the traditional peak regulation strategy is out of touch with the actual load and output trend can be avoided, and the stability of energy storage balance regulation can be improved. By performing tolerance adaptive control according to the hierarchical limiting inflection point and the regulation guidance strategy, the dynamic adaptive adjustment of power quality under the composite disturbance scenario can be realized, the control accuracy and economy can be balanced, the problems of excessive adjustment or insufficient adjustment in traditional control can be solved, and the operation reliability of the power distribution network under complex conditions can be improved.

[0085] In summary, the technical scheme adopted by the application can perform tolerance adaptive control on power quality of a power distribution network in a composite disturbance scenario, so as to improve power quality management and control efficiency under participation of an energy storage system.

[0086] In an embodiment, the application provides an energy storage system-based power quality control system. Figure 4 As shown in the figure, the figure is a module structure diagram of an energy storage system-based power quality control system according to the embodiment of the application, the control system comprises:

[0087] An information acquisition module 100 is configured to acquire dynamic distortion information of voltage and current of a power distribution network in a power grid intelligent dispatching platform.

[0088] A dispatching compensation module 200 is configured to identify a cooperative disturbance attribute of voltage sag and three-phase imbalance through the dynamic distortion information, perform dispatching limiting compensation on the cooperative disturbance attribute, and obtain a layered limiting inflection point of intelligent dispatching of electric energy in the energy storage system in a load storage balance constraint region.

[0089] A fusion correction module 300 is configured to determine a circulating current overrun deviation of state of charge adjustment based on load and electric energy output trend in a power grid, perform fusion correction on the circulating current overrun deviation, generate a dynamic peak regulation criterion of energy storage transfer adjustment of the power grid in a multi-time scale, and then determine a regulation and control guide strategy of electric energy balance regulation and control in the energy storage system according to the dynamic peak regulation criterion.

[0090] An adaptive control module 400 is configured to perform tolerance adaptive control on power quality of a power distribution network in a composite disturbance scenario according to the layered limiting inflection point and the regulation and control guide strategy.

[0091] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0092] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer readable storage medium, including Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other medium that can be used to carry or store data which can be read by a computer.

[0093] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

Claims

1. A power quality control method based on an energy storage system, characterized by, The control method comprises the following steps: Obtain the dynamic distortion information of the voltage and current of the power distribution network in the power grid intelligent scheduling platform; Identify the cooperative disturbance attribute of voltage sag and three-phase imbalance through the dynamic distortion information, schedule the limit compensation of the cooperative disturbance attribute, and obtain the hierarchical limit inflection point of the intelligent scheduling of the electric energy in the energy storage system in the load-storage balance constraint region; Determine the circulating current exceeding deviation of the state of charge adjustment based on the load and the electric energy output trend in the power grid, fuse and correct the circulating current exceeding deviation, generate the dynamic peak shaving criterion of the energy storage transfer adjustment of the power grid in multiple time scales, and then determine the regulation and control guide strategy of the electric energy balance regulation and control in the energy storage system according to the dynamic peak shaving criterion; Adaptively control the power quality of the power distribution network under the composite disturbance scene according to the hierarchical limit inflection point and the regulation and control guide strategy.

2. A power quality control method based on an energy storage system as claimed in claim 1, characterized in that, Identifying the cooperative disturbance attribute of voltage sag and three-phase imbalance through the dynamic distortion information specifically comprises: Extracting the cooperative disturbance characteristics representing voltage sag and three-phase imbalance according to the dynamic distortion information; Determining the disturbance bias mode of voltage sag and three-phase imbalance based on the cooperative disturbance characteristics; Identifying the cooperative disturbance attribute of voltage sag and three-phase imbalance according to the cooperative disturbance characteristics and the disturbance bias mode.

3. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, The hierarchical limit inflection point refers to the power regulation threshold point corresponding to different state of charge intervals when the energy storage system is intelligently scheduled in the load-storage balance constraint region.

4. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, Determine the circulating current exceeding deviation of the state of charge adjustment based on the load and the electric energy output trend in the power grid specifically comprises: Obtain the load change data and the electric energy output trend data in the power grid; Determine the alienated circulating current sequence of the state of charge adjustment based on the load change data and the electric energy output trend data; Extract the circulating current exceeding deviation of the state of charge adjustment based on the load and the electric energy output trend in the power grid from the alienated circulating current sequence.

5. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, The electric energy output trend refers to the law of the change of the power output of the power supply with time.

6. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, The energy storage transfer adjustment refers to the process of redistributing electric energy among different time periods and regions by changing the charging and discharging state and power of the energy storage device to balance supply and demand.

7. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, Determine the regulation and control guide strategy of the electric energy balance regulation and control in the energy storage system according to the dynamic peak shaving criterion specifically comprises: Determine the regulation and control adaptation gradient of the electric energy balance regulation and control of the energy storage system according to the dynamic peak shaving criterion; Construct the steady-state distribution trajectory of the electric energy balance regulation and control of the energy storage system according to the regulation and control adaptation gradient; Determine the regulation and control guide strategy of the electric energy balance regulation and control in the energy storage system according to the steady-state distribution trajectory.

8. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, The electric energy balance regulation and control refers to the process of balancing the supply and demand of electric energy in time and space by adjusting the charging and discharging state and power of the energy storage system.

9. A power quality control method based on an energy storage system as claimed in claim 1, characterized by, The composite disturbance scene refers to the operating condition in which multiple power quality disturbances such as voltage sag, three-phase imbalance and the like are superimposed in the power distribution network.

10. A power quality control system based on energy storage system for performing a power quality control method based on energy storage system according to any one of claims 1 to 9, characterized in that, The control system comprises: An information acquisition module for acquiring the dynamic distortion information of the voltage and current of the power distribution network in the power grid intelligent scheduling platform; The scheduling compensation module is configured to identify a coordinated disturbance attribute of the voltage sag and the three-phase imbalance through the dynamic distortion information, perform scheduling amplitude limiting compensation on the coordinated disturbance attribute, and obtain a layered amplitude limiting inflection point of the electric energy in the energy storage system when the electric energy is intelligently scheduled in a load storage balancing constraint region. The fusion correction module is configured to determine a circulating current out-of-limit deviation when the state of charge is adjusted based on the load and the electric energy output trend in the power grid, perform fusion correction on the circulating current out-of-limit deviation, generate a dynamic peak regulation criterion of the power grid when energy storage is transferred and adjusted in multiple time scales, and then determine a regulation and control guide strategy of the electric energy balance regulation and control in the energy storage system according to the dynamic peak regulation criterion. The adaptive control module is configured to perform tolerance adaptive control on the power quality of the distribution network under the composite disturbance scene according to the layered amplitude limiting inflection point and the regulation and control guide strategy.

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