A power quality control method and system based on energy storage system

Abstract

The application provides a power quality control method and system based on an energy storage system, relates to the technical field of power quality control, and identifies the coordinated disturbance attribute of voltage sag and three-phase imbalance through dynamic distortion information, schedules, limits and compensates the coordinated disturbance attribute, obtains the layered limiting inflection point of the intelligent scheduling of the electric energy in the energy storage system in the load and storage balancing constraint region, fuses and corrects the loop current overrun deviation, generates the dynamic peak regulation criterion of the energy storage transfer adjustment of the power grid in multiple time scales, and then determines the 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 power quality of the distribution network under the composite disturbance scene is controlled according to the layered limiting inflection point and the regulation and control guide strategy. The application can control the power quality of the distribution network under the composite disturbance scene, so as to improve the power quality management and control efficiency of the energy storage system.

Description

Technical Field

[0001] This application relates to the field of power quality control technology, and more specifically, to a power quality control method and system based on an energy storage system. Background Technology

[0002] Power quality control is one of the core technologies for ensuring the safe and stable operation of power systems. It aims to maintain power parameters such as voltage, current, and frequency within permissible ranges through monitoring, analysis, and regulation, reducing the impact of various disturbances on power equipment and user electricity consumption. Traditional compensation devices can only address specific disturbances individually, while power quality control in energy storage systems can achieve complex and appropriate matching. Based on the characteristics of disturbance superposition and time scale, tolerance control rules are formulated to ensure normal equipment operation while avoiding excessive adjustment losses. Power quality control is widely used in new energy distribution networks and sensitive load power supply scenarios. By improving the accuracy and flexibility of disturbance response, it meets the demand for high-quality electricity and is an important means of enhancing the resilience of modern power grids and optimizing power quality.

[0003] However, existing technologies mostly design control strategies for single disturbances, failing to fully identify the combined disturbance attributes of voltage sags and three-phase imbalances. Furthermore, energy storage dispatch does not incorporate load-storage balance constraints to construct a hierarchical limiting mechanism, and it neglects the impact of circulating current exceeding limits in load and power output trend regulation. This results in a lack of precise boundaries for energy storage system regulation under complex disturbances, leading to insufficient adaptability of power balance control strategies to actual disturbance scenarios. Consequently, the power quality control accuracy and system stability of the distribution network cannot meet the requirements of scenarios with high proportions of energy access. Therefore, how to implement tolerance-adaptive control of distribution network power quality under complex disturbance scenarios to improve the power quality management efficiency with the participation of energy storage systems is a problem facing the industry. Summary of the Invention

[0004] This application provides a power quality control method and system based on an energy storage system, which can perform tolerance-adaptive control on the power quality of the distribution network under complex disturbance scenarios, thereby improving the power quality management efficiency with the participation of the energy storage system.

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

[0006] To obtain dynamic distortion information of voltage and current in the distribution network from the smart grid dispatching platform;

[0007] By identifying the cooperative disturbance attributes of voltage sag and three-phase imbalance through the dynamic distortion information, scheduling and limiting compensation is performed on the cooperative disturbance attributes to obtain the hierarchical limiting inflection point of the power in the energy storage system when intelligent scheduling is carried out in the load-storage balance constraint region.

[0008] The circulating current over-limit deviation is determined when the state of charge is regulated based on the load and power output trends in the power grid. The circulating current over-limit deviation is fused and corrected to generate a dynamic peak-shaving criterion for the power grid to adjust energy storage transfer in multiple time scales. Then, the dynamic peak-shaving criterion is used to determine the regulation guidance strategy for power balance regulation in the energy storage system.

[0009] Based on the aforementioned hierarchical limiting inflection point and the aforementioned regulation guidance strategy, the power quality of the distribution network under complex disturbance scenarios is subjected to tolerance-adaptive control.

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

[0011] Based on the dynamic distortion information, the cooperative disturbance characteristics of voltage sag and three-phase imbalance are extracted;

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

[0013] The cooperative disturbance attributes during voltage sag and three-phase imbalance are identified based on the cooperative disturbance characteristics and the disturbance bias mode.

[0014] In this embodiment, the stratified limiting inflection point refers to the power adjustment threshold point corresponding to different state of charge intervals when the energy storage system performs intelligent scheduling within the load-storage balance constraint area.

[0015] In this embodiment, determining the circulating current over-limit deviation when adjusting the state of charge based on load and power output trends in the power grid specifically includes:

[0016] Acquire 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, a heterogeneous circulation sequence is determined during state of charge regulation;

[0018] Extract the over-limit deviation of the circulating current when adjusting the state of charge based on the load and power output trends in the power grid from the heterogeneous circulating current sequence.

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

[0020] In this embodiment, the energy storage transfer adjustment refers to the process of redistributing electrical energy across different time periods and regions by changing the charging and discharging state and power of the energy storage device, in order to balance supply and demand.

[0021] In this embodiment, the regulation guidance strategy for determining the power balance regulation in the energy storage system based on the dynamic peak shaving criterion specifically includes:

[0022] The control adaptation gradient of the power balance regulation in the energy storage system is determined based on the dynamic peak shaving criterion.

[0023] Construct the steady-state distribution trajectory of the energy balance regulation of the energy storage system based on the aforementioned regulation adaptation gradient;

[0024] The control guidance strategy for power balance regulation in the energy storage system is determined based on the steady-state distribution trajectory.

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

[0026] In this embodiment, the composite disturbance scenario refers to the operating condition in which multiple power quality disturbances such as voltage dips and three-phase imbalances occur simultaneously in the distribution network.

[0027] Secondly, this application provides a power quality control system based on an energy storage system for executing a power quality control method based on an energy storage system, the control system comprising:

[0028] The information acquisition module is used to acquire dynamic distortion information of voltage and current in the distribution network in the smart grid dispatching platform;

[0029] The scheduling compensation module is used to identify the cooperative disturbance attributes of voltage sag and three-phase imbalance through the dynamic distortion information, and to perform scheduling limiting compensation on the cooperative disturbance attributes to obtain the hierarchical limiting inflection point of the power in the energy storage system when intelligent scheduling is carried out in the load-storage balance constraint region.

[0030] The fusion correction module is used to determine the circulating current over-limit deviation when adjusting the state of charge based on the load and power output trend in the power grid, perform fusion correction on the circulating current over-limit deviation, generate dynamic peak shaving criteria when the power grid performs energy storage transfer adjustment in multiple time scales, and then determine the control guidance strategy when the energy storage system performs power balance control based on the dynamic peak shaving criteria.

[0031] The adaptation control module is used to perform tolerance adaptation control of the power quality of the distribution network under complex disturbance scenarios based on the hierarchical limiting inflection point and the regulation guidance strategy.

[0032] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0033] The system acquires dynamic distortion information of voltage and current in the distribution network from the smart grid dispatching platform; identifies the cooperative disturbance attributes of voltage sag and three-phase imbalance through the dynamic distortion information, performs dispatching limiting compensation on the cooperative disturbance attributes, and obtains the hierarchical limiting inflection point when the power in the energy storage system is intelligently dispatched in the load-storage balance constraint area; determines the circulating current over-limit deviation when the state of charge is adjusted based on load and power output trends in the power grid, performs fusion correction on the circulating current over-limit deviation, generates a dynamic peak-shaving criterion for the power grid to adjust energy storage transfer in multiple time scales, and then determines the control guidance strategy for power balance regulation in the energy storage system based on the dynamic peak-shaving criterion; and performs tolerance-adaptive control of power quality in the distribution network under complex disturbance scenarios based on the hierarchical limiting inflection point and the control guidance strategy.

[0034] Therefore, this application demonstrates that, given the limitations of existing energy storage-based power quality control systems in sensing complex disturbances and employing simplistic control strategies, it can improve the accuracy of power quality management in distribution networks. Specifically, by acquiring dynamic distortion information of voltage and current in the distribution network from the smart grid dispatching platform, it can comprehensively capture abnormal voltage and current characteristics, providing complete and real-time data for collaborative disturbance analysis. This addresses the fragmentation of traditional monitoring information and significantly enhances the ability to perceive abnormal power quality states. By identifying the collaborative disturbance attributes of voltage sags and three-phase imbalances and obtaining tiered limiting inflection points, it can overcome the limitations of single disturbance analysis. Combined with energy storage load-storage balance constraints, it can formulate differentiated adjustment thresholds, breaking the limitations of traditional fixed limiting and improving the compensation accuracy and safety of energy storage systems under complex disturbances. By determining circulating current over-limit deviations, generating dynamic peak-shaving criteria and control guidance strategies, it can correct circulating current risks in energy storage regulation, forming dispatching rules adaptable to multiple time scales. This avoids the problem of traditional peak-shaving strategies being out of sync with actual load and output trends, improving the stability of energy storage balance control. By implementing tolerance-adaptive control based on hierarchical limiting inflection points and regulation guidance strategies, dynamic adaptation and adjustment of power quality can be achieved under complex disturbance scenarios, balancing control accuracy and economy, solving the problems of over-adjustment or under-adjustment in traditional control, and improving the operational reliability of the distribution network under complex operating conditions.

[0035] In summary, the technical solution adopted in this application can perform tolerance-adaptive control of power quality in distribution networks under complex disturbance scenarios, thereby improving the power quality management efficiency with the participation of energy storage systems. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is an exemplary flowchart of a power quality control method based on an energy storage system provided in this application;

[0038] Figure 2 This is a flowchart illustrating the process for determining the inflection point of layered amplitude limiting, provided in this application.

[0039] Figure 3 This is a flowchart illustrating the process for determining dynamic peak-shaving criteria provided in this application;

[0040] Figure 4 This is a modular structure diagram of a power quality control system based on an energy storage system provided in this application. Detailed Implementation

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

[0042] This application provides a power quality control method and system based on an energy storage system. The core of this method is to acquire dynamic distortion information of the distribution network voltage and current in a smart grid dispatching platform; identify the cooperative disturbance attributes of voltage sags and three-phase imbalances using the dynamic distortion information; perform dispatch limiting compensation on the cooperative disturbance attributes to obtain the hierarchical limiting inflection point when the power in the energy storage system is intelligently dispatched within the load-storage balance constraint region; determine the circulating current exceeding the limit deviation when adjusting the state of charge based on load and power output trends in the power grid; perform fusion correction on the circulating current exceeding the limit deviation to generate a dynamic peak-shaving criterion for energy storage transfer adjustment in the power grid across multiple time scales; and then determine the control guidance strategy for power balance regulation in the energy storage system based on the dynamic peak-shaving criterion; and perform tolerance-adaptive control of the power quality of the distribution network under complex disturbance scenarios based on the hierarchical limiting inflection point and the control guidance strategy.

[0043] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1As shown in the figure, this is an exemplary flowchart of a power quality control method based on an energy storage system according to this embodiment of the present application. The control method includes the following steps:

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

[0045] In practical implementation, firstly, high-precision synchronous phasor measurement devices and smart meters are deployed at key nodes of the distribution network in the energy storage system. These key nodes can be substation outgoing lines, important load connection points, or distributed power grid connection points. The sampling frequency of these devices is no less than 2kHz to ensure the capture of high-frequency distortion signals. Then, the devices collect instantaneous voltage and current data in real time and transmit the data to the real-time database of the power grid intelligent dispatch platform through a dedicated power communication network. The power grid intelligent dispatch platform preprocesses the received data, including noise removal (using an adaptive Kalman filter algorithm to adjust the filter parameters by estimating the noise variance in real time to filter out random interference signals) and data synchronization (calibrating the sampling data of different nodes to the same time axis based on the second pulse signal provided by the Global Positioning System, with the error controlled within 10 microseconds). Finally, a dynamic dataset containing parameters such as voltage sag amplitude, duration, three-phase unbalance, and harmonic content is formed. This dataset serves as the dynamic distortion information of distribution network voltage and current in the power grid intelligent dispatch platform.

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

[0047] In step S2, the coordinated disturbance attributes of voltage sag and three-phase imbalance are identified through the dynamic distortion information, and the coordinated disturbance attributes are compensated by scheduling and limiting to obtain the hierarchical limiting inflection point of the energy storage system when the power is intelligently scheduled in the load-storage balance constraint region.

[0048] In this embodiment, identifying the cooperative disturbance attributes of voltage sag and three-phase imbalance using the dynamic distortion information can be achieved through the following steps:

[0049] Based on the dynamic distortion information, the cooperative disturbance characteristics of voltage sag and three-phase imbalance are extracted;

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

[0051] The cooperative disturbance attributes during voltage sag and three-phase imbalance are identified based on the cooperative disturbance characteristics and the disturbance bias mode.

[0052] In practical implementation, firstly, raw data such as the instantaneous voltage value and duration of each phase are extracted from the dynamic distortion information. For voltage sags, the sag depth (the percentage of the actual voltage difference from the rated voltage to the rated value), start and end times, and fluctuation amplitude of each phase 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 negative to positive sequence components) 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 between sag depth and negative sequence component, and the sag duration and the rate of change of phase sequence imbalance degree. The calculated results are used as the cooperative disturbance characteristics characterizing voltage sags and three-phase imbalance. Then, the cooperative disturbance characteristics are input into an analysis model trained based on historical data. The influence coefficient of sag depth on negative sequence component and the extension coefficient of phase sequence imbalance degree on sag duration are calculated. The influence coefficient is determined by the ratio of the change in sag depth to the change in negative sequence component, and the extension coefficient is the increase in sag duration for every 1% increase in phase sequence imbalance degree. If the influence coefficient is positive and large, it is determined to be "voltage sag-dominated bias"; if the extension coefficient is positive and large, it is determined to be "three-phase imbalance-dominated bias"; if the influence coefficient and extension coefficient are small and alternate between positive and negative, it is determined to be "no obvious dominant bias", thus obtaining the disturbance bias mode under voltage sag and three-phase imbalance. Finally, a multi-dimensional identification framework is constructed, combining cooperative disturbance characteristics and disturbance bias mode. The superposition intensity index is calculated, which is a weighted sum of standardized parameters, with weights determined according to the historical impact degree, reflecting the severity of superposition. The scope of influence is determined according to the bias mode: when voltage sag is dominant, the affected load area is determined by combining the sag depth distribution; when three-phase imbalance is dominant, the affected nodes are determined based on the phase sequence imbalance propagation characteristics. By combining time parameters and the duration of the bias mode, the instantaneous, short-term, or long-term superposition type is determined, and the information is integrated to identify the specific cooperative disturbance attributes, thus obtaining the cooperative disturbance attributes under voltage sag and three-phase imbalance.

[0053] It should be noted that, in this application, voltage sag refers to a power quality problem in a power system where the voltage suddenly drops and lasts for a certain period of time; three-phase imbalance refers to a state in which the amplitude or phase of the voltage and current in each phase of a three-phase power system differs; cooperative 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 biased law of the influence of one on the other when voltage sag and three-phase imbalance are superimposed; cooperative disturbance attributes refer to the overall characteristics exhibited when voltage sag and three-phase imbalance are superimposed.

[0054] Preferably, in this embodiment, scheduling limiting compensation is performed on the cooperative disturbance attribute to obtain the hierarchical limiting inflection point when the power in the energy storage system is intelligently scheduled in the load-storage balance constraint region, with reference to... Figure 2As shown in the figure, this is a flowchart illustrating the process of determining the inflection point of layered clipping inflection in some embodiments of this application. In this embodiment, determining the inflection point of layered clipping can be achieved by the following steps:

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

[0056] In step S22, the constraint limiting characteristics of electrical energy in the energy storage system under load-storage balance constraints are determined based on all disturbance impact indicators.

[0057] In step S23, the limiting demand description of the power in the energy storage system during intelligent scheduling in the load-storage balance constraint region is determined according to the constraint limiting characteristics.

[0058] In step S24, the hierarchical limiting inflection point is determined based on the limiting demand description when the electrical energy in the energy storage system is intelligently scheduled in the load-storage balance constraint region.

[0059] In practice, the process begins by extracting various disturbance factors from the cooperative disturbance attributes, including the voltage sag depth and duration, the negative sequence component of three-phase imbalance, and the phase sequence imbalance degree. For each factor, an impact assessment dimension is defined, such as the deviation from energy storage charging and discharging power and the reduction rate of state-of-charge (SOC) regulation accuracy. A correlation model between each factor and its impact dimension is established using historical operational data. The actual values ​​of each factor are substituted into the model to calculate the quantified value of each factor in different dimensions. Then, a weighted summation (with weights set according to the actual impact of each factor on scheduling) yields the disturbance impact index for each disturbance factor. Next, all disturbance impact indices are collected and categorized by impact type, such as power fluctuations and SOC deviations. For each type of index, the limiting threshold when the index exceeds the normal range is calculated, based on the energy storage system's load-storage balance constraint parameters (such as upper and lower limits of SOC and maximum charging and discharging power). For example, when a power fluctuation index exceeds a set value, the corresponding maximum allowable charging and discharging power adjustment range is determined; when a SOC deviation index exceeds the limit, the emergency adjustment range of the SOC is determined. These thresholds and ranges are integrated to form constraint limiting features, including power limitation ranges and state of charge (SCC) adjustment boundaries, which are the constraint limiting features of electrical energy in the energy storage system under load-storage balance constraints. Then, the power limitation ranges and SCC adjustment boundaries within these constraint limiting features are analyzed. Regarding power, the limitation ranges describe the allowable charging and discharging power ranges under different disturbances; for example, when the disturbance is small, the charging and discharging power can be between 60% and 80% of the rated power. Regarding SCC, the adjustment boundaries describe the limiting requirements under different states; for example, when the SCC is below 30%, the discharging power is limited. These contents are categorized according to scheduling scenarios, and the classification results serve as the limiting requirement description for intelligent scheduling of electrical energy in the load-storage balance constraint area of ​​the energy storage system. Finally, based on the limiting requirements under different SCC states in the limiting requirement description, the SCC state of the load-storage balance constraint area is divided into three ranges: high, medium, and low. For each range, combined with the power range in the limiting requirements, the power adjustment threshold point within each range is determined through simulation (simulating the effect of adjusting power according to different SCC states). For example, in the high charge state range (70%–90%), the maximum allowable discharge power threshold is determined based on the limiting requirements as the lower limit inflection point of this range; in the medium charge state range (30%–70%), the upper and lower thresholds of charging and discharging power are determined as inflection points; in the low charge state range (10%–30%), the maximum charging power threshold is determined as the upper limit inflection point, and finally, the hierarchical limiting inflection points of the energy storage system when the power is intelligently scheduled in the load-storage balance constraint area are obtained.

[0060] It should be noted that, in this application, the load-storage balance constraint region refers to the operating range in which the state of charge of the energy storage system is maintained within a safe range and the charging and discharging power does not exceed the limit; the disturbance impact index refers to the parameter that quantifies the degree of influence of each disturbance factor on the energy dispatching process of the energy storage system; the constraint limiting characteristics refer to the power, state of charge, and other limiting characteristics that the energy storage system must follow during the energy dispatching process under the load-storage balance constraint; the limiting requirement description refers to the limiting conditions that the energy storage system must meet when performing intelligent dispatching within the load-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 performs intelligent dispatching within the load-storage balance constraint region.

[0061] In step S3, the circulating current over-limit deviation in the power grid when adjusting the state of charge based on load and power output trends is determined, the circulating current over-limit deviation is fused and corrected, and a dynamic peak-shaving criterion is generated for the power grid to adjust energy storage transfer in multiple time scales. Then, the control guidance strategy for power balance control in the energy storage system is determined by the dynamic peak-shaving criterion.

[0062] In this embodiment, the following steps can be used to determine the circulating current over-limit deviation when adjusting the state of charge based on load and power output trends in the power grid:

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

[0064] Based on the load change data and the power output trend data, a heterogeneous circulation sequence is determined during state of charge regulation;

[0065] Extract the over-limit deviation of the circulating current when adjusting the state of charge based on the load and power output trends in the power grid from the heterogeneous circulating current sequence.

[0066] In practical implementation, firstly, intelligent monitoring terminals are installed at each load node of the power grid. These terminals can collect real-time power data of various loads, including industrial, residential, and commercial loads, at 15-minute intervals, forming load change data. Through the monitoring system of new energy power plants, real-time output data of photovoltaic and wind power are collected. Combined with historical data from the same period and weather forecast information, time series analysis is used to generate power output trend data for the next 24 hours (details omitted here). Next, a state-of-load (SOL) regulation model is constructed. The load change data and power output trend data are input to calculate the energy storage charging and discharging power required to maintain grid power balance at different times. Based on the grid topology, a power flow calculation method is used to substitute the calculated charging and discharging power into the grid model to solve for the circulating current value at each time point. These circulating current values ​​are compared with the baseline circulating current value during normal operation (without SOL regulation) to obtain the circulating current deviation at each time point. These deviations are then arranged chronologically to form a heterogeneous circulating current sequence during SOL regulation. Finally, based on grid equipment parameters (such as the rated withstand values ​​of cables and transformers), the circulating current safety limit is determined. The heterogeneous circulating current sequence is traversed, and the circulating current value at each time point is compared with the safety limit. All circulating current data exceeding the limit are filtered out. For each data exceeding the limit, the difference between it and the safety limit (i.e., the exceeding value minus the limit) is calculated. These differences are then arranged in chronological order to form the circulating current over-limit deviation when adjusting the state of charge based on load and power output trends in the power grid.

[0067] It should be noted that, in this application, power output trend refers to the law of change of power output over time; load change data refers to the quantitative information of the dynamic changes of various types of power loads in the power grid over time; power output trend data refers to the information on the law of change of power output over time; heterogeneous circulating current sequence refers to the sequence of circulating currents in the power grid that deviate from the normal operating state over time during the process of adjusting the state of charge of energy storage according to the load and power output trend; circulating current over-limit deviation refers to the difference between the portion exceeding the circulating current limit allowed for 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-shaving criterion for the power grid to adjust energy storage transfer across multiple time scales, with reference to... Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining dynamic peak shaving criteria in some embodiments of this application. In this embodiment, determining dynamic peak shaving criteria can be achieved through the following steps:

[0069] In step S31, dynamic operation decisions of the power grid at multiple time scales are collected;

[0070] In step S32, the transfer delay rule for energy storage transfer adjustment is determined based on the circulating flow over-limit deviation;

[0071] In step S33, the dynamic operation decision is mapped to the transfer delay rule to obtain the elastic peak-shaving constraint of the power grid when adjusting energy storage transfer in multiple time scales.

[0072] In step S34, the dynamic peak-shaving criterion for the power grid to adjust energy storage transfer at multiple time scales is determined based on the elastic peak-shaving constraint.

[0073] In practical implementation, firstly, three time scales are defined: second-level, minute-level, and hour-level. Decision data for each scale, including load allocation instructions, energy storage charging and discharging plans, and power output adjustment schemes, are extracted from the real-time database of the intelligent power grid dispatching system. The data is timestamped and formatted uniformly, and stored according to time scale, forming dynamic operation decisions for the power grid across multiple time scales. Next, circulating current exceedance deviations are categorized into three levels based on magnitude: minor (≤10% of the limit), moderate (10%–30%), and severe (>30%). For each deviation level, a safe adjustment delay time is calculated based on historical data, such as a 5-second delay for minor deviations, a 10-second delay for moderate deviations, and a 15-second delay for severe deviations. Rules are established including: increasing the delay time as the deviation level increases, and recalculating the delay based on the current level when the deviation decreases. These rules serve as the transfer delay rules for energy storage transfer adjustments. Finally, the dynamic operation decisions and transfer delay rules are matched according to the time scale. Second-level decisions correspond to severe deviation delay rules, limiting single adjustment power to ≤20% of rated power; minute-level decisions correspond to moderate deviation rules, allowing adjustment power between 20% and 50%, with an interval of ≥10 seconds; hour-level decisions correspond to slight deviation rules, allowing adjustment power up to 50% to 80%, with a 5-second delay. Integrating the power ranges and time intervals at each scale forms a flexible peak-shaving constraint for the power grid to adjust energy storage transfer across multiple time scales. Finally, trigger conditions for each time scale are extracted from the flexible peak-shaving constraints: Second-level: Peak shaving is triggered when real-time power fluctuation ≥10% of rated power and circulating current deviation reaches a severe level; Minute-level: Peak shaving is triggered when the average power deviation within 5 minutes ≥5% and the deviation is moderate; Hour-level: Peak shaving is triggered when the predicted load gap ≥8% after 1 hour and the deviation is slight. Binding these conditions to the corresponding adjustment power and delay time forms a dynamic peak-shaving criterion for the power grid to adjust energy storage transfer across multiple time scales.

[0074] It should be noted that, in this application, energy storage transfer adjustment refers to the process of redistributing electrical energy across different time periods and regions by changing the charging and discharging state and power of energy storage devices to balance supply and demand; dynamic operation decision refers to the real-time dispatch strategy formulated by the power grid at different time scales to maintain stable operation; transfer delay rule refers to the delay time specification set for energy storage transfer adjustment operation to avoid the expansion of circulating current exceeding the limit; flexible peak shaving constraint refers to the scope and conditions under which energy storage transfer adjustment is allowed at multiple time scales; dynamic peak shaving criterion refers to the specific conditions and thresholds for initiating energy storage transfer adjustment at multiple time scales.

[0075] In this embodiment, the control guidance strategy for determining the power balance control in the energy storage system based on the dynamic peak-shaving criterion can be implemented using the following steps:

[0076] The control adaptation gradient of the power balance regulation in the energy storage system is determined based on the dynamic peak shaving criterion.

[0077] Construct the steady-state distribution trajectory of the energy balance regulation of the energy storage system based on the aforementioned regulation adaptation gradient;

[0078] The control guidance strategy for power balance regulation in the energy storage system is determined based on the steady-state distribution trajectory.

[0079] In practical implementation, firstly, the time scale (seconds, minutes, hours) and trigger thresholds (such as power fluctuation ratio and circulating current deviation level) in the dynamic peak-shaving criteria are analyzed. Gradients are divided according to the urgency of peak shaving: second-level criteria correspond to the first-level gradient (high-intensity control), with a single power adjustment range of 30%–50% of the rated power; minute-level criteria correspond to the second-level gradient (medium-intensity control), with an adjustment range of 10%–30%; and hour-level criteria correspond to the third-level gradient (low-intensity control), with an adjustment range of 5%–10%. Response time is matched for each gradient: first-level gradient response time ≤ 1 second, second-level ≤ 10 seconds, and third-level ≤ 60 seconds, thus obtaining the control adaptation gradient for energy balance regulation in the energy storage system. Then, based on the adjustment range and response time of each control adaptation gradient, combined with energy storage state of charge constraints (such as upper and lower limits of state of charge), a piecewise linear programming method is used to construct the trajectory. Under the first-level gradient, power nodes are set at 1-second intervals, ensuring that the power difference between adjacent nodes is ≤10% of the rated power. Under the second-level gradient, nodes are set at 10-second intervals, with a power difference ≤5%. Under the third-level gradient, nodes are set at 60-second intervals, with a power difference ≤3%. The trajectory needs to cover the entire time period from the triggering of the peak-shaving criterion to its end, ensuring continuous power changes within the gradient's allowable range, thus obtaining the steady-state distribution trajectory for the energy storage system's power balance control. Finally, the steady-state distribution trajectory is decomposed into specific operational instructions at time nodes. Each node specifies the energy storage charging / discharging state (charging / discharging / standby), real-time power value, and duration. For example, under the first-level gradient, a node instruction might be "discharge, 30% rated power, lasting 1 second." Combining equipment operating parameters (such as maximum switching frequency), state switching conditions (such as switching to charging when the state of charge drops to 20%) are added to the instruction. All node instructions are sorted by time to form a time-segmented, executable control guidance strategy, i.e., the control guidance strategy for power balance control in the energy storage system.

[0080] It should be noted that, in this application, power balance regulation refers to the process of maintaining a balance between power supply and demand in the power grid in time and space by adjusting the charging and discharging state and power of the energy storage system; regulation adaptation gradient refers to a sequence of levels that are divided according to different dynamic peak-shaving demands and matched with the intensity of energy storage regulation; steady-state distribution trajectory refers to the stable path of the charging and discharging power of the energy storage system over time under each regulation adaptation gradient; and regulation guidance strategy refers to the specific operational plan that guides the energy storage system to carry out power balance regulation.

[0081] In step S4, the power quality of the distribution network under complex disturbance scenarios is subjected to tolerance-adaptive control based on the hierarchical limiting inflection point and the regulation guidance strategy.

[0082] In specific implementation, the tolerance-adaptive control of power quality in the distribution network under complex disturbance scenarios, based on the aforementioned tiered limiting inflection point and the aforementioned regulation guidance strategy, can be achieved in the following way: First, set the power quality tolerance range of the distribution network under complex disturbance scenarios, including: voltage sag depth not exceeding 10%, three-phase imbalance not exceeding 2%, etc. Real-time monitoring of the distribution network's voltage, current, power, and other operating data, while simultaneously acquiring the state of charge and operating parameters of the energy storage system. Compare the monitoring data with the tolerance range, and when a disturbance occurs within the tolerance range, determine the allowable adjustable power range of the energy storage system based on the tiered limiting inflection point, and select the specific charging and discharging method and regulation rhythm based on the regulation guidance strategy. For example, when a voltage sag depth of 8% and a three-phase imbalance of 1.5% are detected, if the energy storage is in a medium-charge state range, discharge regulation is performed within the range of 50% to 70% of the rated power according to the regulation guidance strategy. Continuously track changes in disturbances and dynamically adjust the regulation intensity to ensure that the power quality operating parameters remain stable within the tolerance range, thus achieving tolerance-adaptive control of power quality. This will not be elaborated further here.

[0083] It should be noted that, in this application, the composite disturbance scenario refers to the operating condition in which multiple power quality disturbances such as voltage dips and three-phase imbalances occur simultaneously in the distribution network; tolerance adaptation control refers to the control method that dynamically adjusts the system to adapt to disturbances within a preset power quality tolerance range.

[0084] Therefore, this application demonstrates that, given the limitations of existing energy storage-based power quality control systems in sensing complex disturbances and employing simplistic control strategies, it can improve the accuracy of power quality management in distribution networks. Specifically, by acquiring dynamic distortion information of voltage and current in the distribution network from the smart grid dispatching platform, it can comprehensively capture abnormal voltage and current characteristics, providing complete and real-time data for collaborative disturbance analysis. This addresses the fragmentation of traditional monitoring information and significantly enhances the ability to perceive abnormal power quality states. By identifying the collaborative disturbance attributes of voltage sags and three-phase imbalances and obtaining tiered limiting inflection points, it can overcome the limitations of single disturbance analysis. Combined with energy storage load-storage balance constraints, it can formulate differentiated adjustment thresholds, breaking the limitations of traditional fixed limiting and improving the compensation accuracy and safety of energy storage systems under complex disturbances. By determining circulating current over-limit deviations, generating dynamic peak-shaving criteria and control guidance strategies, it can correct circulating current risks in energy storage regulation, forming dispatching rules adaptable to multiple time scales. This avoids the problem of traditional peak-shaving strategies being out of sync with actual load and output trends, improving the stability of energy storage balance control. By implementing tolerance-adaptive control based on hierarchical limiting inflection points and regulation guidance strategies, dynamic adaptation and adjustment of power quality can be achieved under complex disturbance scenarios, balancing control accuracy and economy, solving the problems of over-adjustment or under-adjustment in traditional control, and improving the operational reliability of the distribution network under complex operating conditions.

[0085] In summary, the technical solution adopted in this application can perform tolerance-adaptive control of power quality in distribution networks under complex disturbance scenarios, thereby improving the power quality management efficiency with the participation of energy storage systems.

[0086] Example 2: This application provides a power quality control system based on an energy storage system, referencing... Figure 4 As shown in the figure, this is a modular structure diagram of a power quality control system based on an energy storage system according to this embodiment of the present application. The control system includes:

[0087] The information acquisition module 100 is used to acquire dynamic distortion information of voltage and current in the distribution network in the smart grid dispatching platform;

[0088] The scheduling compensation module 200 is used to identify the cooperative disturbance attributes of voltage sag and three-phase imbalance through the dynamic distortion information, and to perform scheduling limiting compensation on the cooperative disturbance attributes to obtain the hierarchical limiting inflection point of the power in the energy storage system when intelligent scheduling is carried out in the load-storage balance constraint region.

[0089] The fusion correction module 300 is used to determine the circulating current over-limit deviation when adjusting the state of charge based on the load and power output trend in the power grid, perform fusion correction on the circulating current over-limit deviation, generate dynamic peak shaving criteria when the power grid performs energy storage transfer adjustment in multiple time scales, and then determine the control guidance strategy when the energy storage system performs power balance control based on the dynamic peak shaving criteria.

[0090] The adaptation control module 400 is used to perform tolerance adaptation control on the power quality of the distribution network under complex disturbance scenarios based on the hierarchical limiting inflection point and the regulation guidance strategy.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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 processor, 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 a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. 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 disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0093] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A power quality control method based on an energy storage system, characterized in that, The control method includes the following steps: To obtain dynamic distortion information of voltage and current in the distribution network from the smart grid dispatching platform; Based on the dynamic distortion information, the cooperative disturbance characteristics representing voltage sag and three-phase imbalance are extracted. Based on the cooperative disturbance characteristics, the disturbance bias mode under voltage sag and three-phase imbalance is determined. Based on the cooperative disturbance characteristics and the disturbance bias mode, the cooperative disturbance attributes under voltage sag and three-phase imbalance are identified. The cooperative disturbance attributes are subjected to scheduling limiting compensation to obtain the hierarchical limiting inflection point when the energy storage system performs intelligent scheduling in the load-storage balance constraint region. The cooperative disturbance attribute refers to the overall characteristics exhibited when voltage sag and three-phase imbalance are superimposed. The hierarchical limiting inflection point refers to the power adjustment threshold point corresponding to different state of charge intervals when the energy storage system performs intelligent scheduling in the load-storage balance constraint region. The system acquires load change data and power output trend data in the power grid. Based on the load change data and power output trend data, it determines the heterogeneous circulating current sequence during state of charge (SCC) regulation. From the heterogeneous circulating current sequence, it extracts the circulating current over-limit deviation during SCC regulation based on load and power output trends in the power grid. It performs fusion correction on the circulating current over-limit deviation to generate a dynamic peak-shaving criterion for energy storage transfer adjustment in the power grid at multiple time scales. Based on the dynamic peak-shaving criterion, it determines the control adaptation gradient for power balance regulation in the energy storage system. Based on the control adaptation gradient, it constructs a steady-state distribution trajectory for power balance regulation in the energy storage system. Based on the steady-state distribution trajectory, it determines the control guidance strategy for power balance regulation in the energy storage system. The circulating current over-limit deviation refers to the difference between the portion exceeding the circulating current limit allowed for safe operation of the power grid and the limit value. The control guidance strategy is a specific operational plan to guide the energy storage system in power balance regulation. Based on the aforementioned hierarchical limiting inflection point and the aforementioned regulation guidance strategy, the power quality of the distribution network under complex disturbance scenarios is subjected to tolerance-adaptive control.

2. The power quality control method based on an energy storage system as described in claim 1, characterized in that, The aforementioned power output trend refers to the pattern of how the power output of the power source changes over time.

3. The power quality control method based on an energy storage system as described in claim 1, characterized in that, The aforementioned energy storage transfer adjustment refers to the process of redistributing electrical energy across different time periods and regions by changing the charging and discharging state and power of energy storage devices in order to balance supply and demand.

4. The power quality control method based on an energy storage system as described in claim 1, characterized in that, The aforementioned power balance regulation refers to the process of maintaining a balance between power supply and demand in the power grid in time and space by adjusting the charging and discharging state and power of the energy storage system.

5. The power quality control method based on an energy storage system as described in claim 1, characterized in that, The aforementioned composite disturbance scenario refers to the operating condition in a distribution network where multiple power quality disturbances, such as voltage dips and three-phase imbalances, are superimposed.

6. A power quality control system based on an energy storage system, used to execute a power quality control method based on an energy storage system as described in any one of claims 1 to 5, characterized in that, The control system includes: The information acquisition module is used to acquire dynamic distortion information of voltage and current in the distribution network in the smart grid dispatching platform; The scheduling compensation module is used to identify the cooperative disturbance attributes of voltage sag and three-phase imbalance through the dynamic distortion information, and to perform scheduling limiting compensation on the cooperative disturbance attributes to obtain the hierarchical limiting inflection point of the power in the energy storage system when intelligent scheduling is carried out in the load-storage balance constraint region. The fusion correction module is used to determine the circulating current over-limit deviation when adjusting the state of charge based on the load and power output trend in the power grid, perform fusion correction on the circulating current over-limit deviation, generate dynamic peak shaving criteria when the power grid performs energy storage transfer adjustment in multiple time scales, and then determine the control guidance strategy when the energy storage system performs power balance control based on the dynamic peak shaving criteria. The adaptation control module is used to perform tolerance adaptation control of the power quality of the distribution network under complex disturbance scenarios based on the hierarchical limiting inflection point and the regulation guidance strategy.

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