A distributed resource collaborative regulation method and system based on power quality constraints

Abstract

This application discloses a method and system for coordinated control of distributed resources based on power quality constraints. The method first extracts power quality indicators (PMIs) from the operational data of distributed resources and grid status data in the distribution network. Then, it constructs a power quality constraint vector based on these PMIs to evaluate the distribution network and determine its power quality level. Next, using the power quality level as a constraint, it dynamically aggregates the distributed resources of the distribution network to form multiple resource clusters. Finally, it performs limit-crossing analysis based on the power quality level to generate hierarchical coordinated control commands for distributed coordinated control of the multiple resource clusters. This method achieves dynamic aggregation and hierarchical coordinated control of distributed resources under power quality level constraints, significantly improving power quality stability and control reliability.

Description

Technical Field

[0001] This application relates to the field of resource regulation technology, specifically to a distributed resource collaborative regulation method and system based on power quality constraints. Background Technology

[0002] With the large-scale integration of various distributed resources, such as distributed renewable energy, controllable loads, and energy storage, into the distribution network, the power grid's operation has shifted from traditional unidirectional power transmission to multi-source, multi-point, bidirectional flow, leading to increasingly complex system structures and operating states. Against this backdrop, power quality issues such as voltage deviation, harmonic distortion, and three-phase imbalance are more easily amplified. Disorderly responses between different types of distributed resources can further exacerbate local node overruns and intensify power quality fluctuations at the point of common coupling. Existing resource regulation methods mostly rely on power balancing or economic dispatch, lacking a coordinated regulation mechanism that incorporates power quality indicators into unified constraints. This makes it difficult to maintain stable power quality in the distribution network while achieving coordinated operation of multiple resources, resulting in low resource utilization efficiency, inconsistent regulation responses, and lagging overrun management methods. Consequently, these methods fail to meet the refined and proactive regulation needs under conditions of high-proportion distributed access. Summary of the Invention

[0003] To address the problem that existing distributed resource regulation lacks unified quantitative management of power quality constraints, making it difficult to balance power quality stability in collaborative control, this application proposes a distributed resource collaborative regulation method and system based on power quality constraints.

[0004] This application is achieved through the following technical solution:

[0005] A distributed resource collaborative regulation method based on power quality constraints includes:

[0006] Collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling;

[0007] Based on the power quality indicators, a power quality constraint vector is constructed to evaluate the distribution network and determine the power quality level.

[0008] Using the power quality level as a constraint, the distributed resources of the distribution network are dynamically aggregated to form multiple resource clusters;

[0009] Based on the power quality level, limit exceedance analysis is performed, and hierarchical collaborative control commands are generated to perform distributed collaborative regulation of multiple resource clusters.

[0010] In some implementations, the process of collecting operational data of distributed resources and grid status data in the distribution network and extracting power quality indicators from the point of common coupling includes:

[0011] Multi-source heterogeneous raw datasets are obtained by synchronously collecting data from multiple types of intelligent sensing terminals deployed at different levels in the power distribution network; wherein the multi-source heterogeneous raw datasets include operational data and power grid status data.

[0012] The multi-source heterogeneous original datasets are aligned and fused to construct a standardized data sequence;

[0013] The standardized data sequence is mapped to the distribution network for location, and the common connection point between the distribution network and the main grid is determined.

[0014] Based on the common connection point, fundamental wave positive and negative sequence component analysis is performed to extract the fundamental voltage component and the fundamental current component.

[0015] Closed-loop tracking is performed on the common connection point, and dynamic separation is performed based on the tracking results to obtain voltage harmonic components and current harmonic components.

[0016] The power quality index is set based on the evaluation of the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component.

[0017] In some embodiments, the step of evaluating and setting the power quality index based on the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component includes:

[0018] Based on the fundamental voltage and fundamental current components, a first type of power quality index characterizing the steady-state operation characteristics of the power grid is calculated.

[0019] Based on the voltage harmonic components and current harmonic components, a second type of power quality index characterizing the waveform distortion characteristics of the power grid is calculated.

[0020] The power quality index is constructed by integrating the first type of power quality index and the second type of power quality index.

[0021] In some implementations, the step of constructing a power quality constraint vector based on the power quality indicators to evaluate the distribution network and determine the power quality level includes:

[0022] The power quality indicators are normalized to obtain the normalized result.

[0023] The normalization result is mapped to a preset interval to determine the initial index value;

[0024] The combined weight coefficients are obtained by linearly weighting the initial index values.

[0025] Multiply the initial index value by the combined weight coefficient to construct a multidimensional power quality constraint vector;

[0026] Based on the multidimensional power quality constraint vector, a fuzzy comprehensive evaluation of the distribution network is performed to generate a comprehensive evaluation value.

[0027] Fluctuation penalties are applied based on the comprehensive evaluation value, and the power quality level is determined based on the penalty factor.

[0028] In some implementations, the step of linearly weighting the initial index values ​​to obtain the combined weight coefficients includes:

[0029] Based on the initial index values, perform hierarchical analysis to determine the subjective weight vector;

[0030] Entropy weight is calculated based on the initial index values ​​to determine the objective weight vector;

[0031] Triggering calculations are performed based on the initial indicator values ​​to obtain the indicator triggering frequency;

[0032] The control effect of the indicators is obtained by analyzing the trigger frequency of the aforementioned indicators.

[0033] Based on the effect of the indicator regulation, preference learning is performed according to the trigger frequency of the indicator to determine the periodic preference coefficient;

[0034] The subjective weight vector and the objective weight vector are linearly weighted and fused according to the periodic preference coefficient to obtain the combined weight coefficient.

[0035] In some implementations, the dynamic aggregation of distributed resources in the distribution network to form multiple resource clusters, using the power quality level as a constraint, includes:

[0036] The power quality level is used as a constraint to quantify the controllability of distributed resources and extract controllable resource information.

[0037] Multidimensional regulation analysis is performed based on the controllable resource information to obtain multidimensional regulation response data; wherein the multidimensional regulation response data includes active power regulation response data and reactive power regulation response data.

[0038] Based on the multidimensional regulation response data, power quality analysis is performed on the distribution network to construct a dynamic aggregated index set.

[0039] Distributed resources are dynamically grouped online according to the aforementioned dynamic aggregation index set, and multiple power quality regulation data groups are defined.

[0040] Multiple power quality regulation data groups are traversed to perform resource matching and generate multiple resource clusters.

[0041] In some implementations, the step of performing limit-crossing analysis based on the power quality level and generating hierarchical collaborative control commands to perform distributed collaborative regulation of multiple resource clusters includes:

[0042] Based on the power quality level, limit violations are identified, and multiple dominant limit violation indicators are determined.

[0043] Based on multiple dominant over-limit indicators, a spatiotemporal impact assessment is performed to generate the over-limit impact range, and multiple adjustment targets are set according to the over-limit impact range.

[0044] Multiple hierarchical control architectures are constructed according to the aforementioned adjustment objectives; wherein, the multiple hierarchical control architectures include hierarchical collaborative control instructions;

[0045] The hierarchical collaborative control instructions are used to perform hierarchical collaborative control on multiple resource clusters.

[0046] In some implementations, the hierarchical collaborative control of multiple resource clusters via the hierarchical collaborative control instructions includes:

[0047] The aforementioned hierarchical control architecture includes a local control layer, a cluster coordination layer, and a control optimization layer;

[0048] According to the hierarchical collaborative control command, multiple hierarchical control architectures are activated, and the local control layer performs local sensing control on the distribution network to generate local power quality data.

[0049] The local power quality data is synchronized to the cluster coordination layer to allocate resources to multiple resource clusters according to multiple adjustment targets, and a resource allocation result is generated.

[0050] The resource allocation results are synchronized to the control optimization layer for multi-scale optimization, and a general control command is generated to perform global coordinated control and optimization of the distributed resources of the distribution network.

[0051] In some implementations, synchronizing the resource allocation results to the control optimization layer for multi-scale optimization and generating a general control command to perform global coordinated control and optimization of the distributed resources of the distribution network includes:

[0052] The resource allocation results are synchronized to the control optimization layer for multi-period allocation security analysis to determine multiple control security domains; wherein, the multiple control security domains include short-term control security domains, medium-term control security domains, and long-term control security domains;

[0053] The short-term control safety domain is used as a limit to optimize and control the resource allocation results, thereby obtaining short-term control indicators;

[0054] The resource allocation results are optimized and controlled by using the medium-term control security domain as a limit to obtain medium-term control indicators.

[0055] The long-term control security domain is used as a constraint to optimize and control the resource allocation results, thereby obtaining long-term control indicators.

[0056] The short-term, medium-term, and long-term control indicators are coupled and analyzed according to time scales to construct the overall control command.

[0057] On the other hand, this application proposes a distributed resource collaborative control system based on power quality constraints, comprising:

[0058] The data extraction unit is used to collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling.

[0059] An evaluation unit is used to construct a power quality constraint vector based on the power quality indicators to evaluate the distribution network and determine the power quality level.

[0060] The resource aggregation unit is used to dynamically aggregate the distributed resources of the distribution network using the power quality level as a constraint to form multiple resource clusters.

[0061] In addition, a control unit is used to perform over-limit analysis based on the power quality level and generate hierarchical collaborative control commands to perform distributed collaborative control of multiple resource clusters.

[0062] This application proposes a distributed resource collaborative control method based on power quality constraints. First, it extracts power quality indicators (PMIs) from the operational data of distributed resources and grid status data in the distribution network. Then, it constructs a power quality constraint vector based on these PMIs to evaluate the distribution network and determine its power quality level. Next, using the power quality level as a constraint, it dynamically aggregates the distributed resources of the distribution network to form multiple resource clusters. Finally, it performs limit-crossing analysis based on the power quality level to generate hierarchical collaborative control commands for distributed collaborative control of the multiple resource clusters. This method achieves dynamic aggregation and hierarchical collaborative control of distributed resources under power quality level constraints, significantly improving power quality stability and control reliability.

[0063] Correspondingly, the distributed resource collaborative control system based on power quality constraints proposed in this application also possesses the same technical effects as described above. Attached Figure Description

[0064] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings:

[0065] Figure 1 This is a schematic diagram of the distributed resource collaborative regulation method proposed in the embodiments of this application;

[0066] Figure 2 This is a block diagram illustrating the principle of the distributed resource collaborative control system proposed in this application.

[0067] Figure 3 This is a schematic diagram of the electronic device structure proposed in the embodiments of this application;

[0068] Figure 4 This is a schematic diagram of a computer-readable storage medium structure proposed in an embodiment of this application.

[0069] Figure reference numerals and corresponding component names:

[0070] 200-Distributed resource collaborative control system, 201-Data extraction unit, 202-Evaluation unit, 203-Resource aggregation unit, 204-Control unit, 300-Electronic device, 310-Memory, 320-Processor, 311-Computer program A, 400-Computer readable storage medium, 411-Computer program B. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0072] like Figure 1 As shown in the figure, this application proposes a distributed resource collaborative control method based on power quality constraints, including the following steps:

[0073] Step 1: Collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling;

[0074] Step 2: Construct a power quality constraint vector based on power quality indicators to evaluate the distribution network and determine the power quality level;

[0075] Step 3: Using power quality level as a constraint, dynamically aggregate the distributed resources of the distribution network to form multiple resource clusters;

[0076] Step 4: Perform limit exceedance analysis based on power quality level, and generate hierarchical collaborative control commands to carry out distributed collaborative regulation of multiple resource clusters.

[0077] Furthermore, in step 1 of this application embodiment, the specific implementation of collecting operational data of distributed resources and power grid status data in the distribution network and extracting power quality indicators of the point of common coupling includes:

[0078] Multi-source heterogeneous raw datasets are obtained by synchronously collecting data from multiple types of intelligent sensing terminals deployed at different levels of the power distribution network; these multi-source heterogeneous raw datasets include operational data and power grid status data.

[0079] Data alignment and fusion are performed on multi-source heterogeneous original datasets to construct standardized data sequences;

[0080] Standardized data sequences are mapped to the distribution network for location, determining the common connection point between the distribution network and the main grid;

[0081] Based on the common connection point, fundamental positive and negative sequence component analysis is performed to extract the fundamental voltage component and the fundamental current component.

[0082] Closed-loop tracking of the common connection point is performed, and dynamic separation is performed based on the tracking results to obtain voltage harmonic components and current harmonic components;

[0083] The power quality index is set based on the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component.

[0084] Furthermore, the specific implementation method for synchronous data collection by deploying multiple types of intelligent sensing terminals at different levels of the distribution network is as follows: multiple types of intelligent sensing terminals such as voltage, current, phase angle, harmonics, and electrical energy can be deployed at different voltage levels and topology levels of the distribution network to synchronously collect the operating status quantities of distributed resources and the grid status quantities of distribution network nodes, forming a multi-source heterogeneous original dataset containing instantaneous voltage, current, positive and negative sequence voltage, current phasor, harmonic content, and steady-state operating parameters. Specifically, on the medium-voltage distribution network side, the distribution terminal units and feeder terminal units in the distribution automation system collect three-phase voltage, three-phase current, active power, reactive power, power factor, and switch status data at the feeder start-up point, sectionalizing switch position, and distributed power source access point. On the low-voltage distribution area side, the distribution area terminal units deployed on the distribution transformer side acquire voltage, current, and power data at the distribution area outlet, and collect operating data of distributed photovoltaic, energy storage systems, electric vehicle charging piles, and large-capacity adjustable loads on the user side through smart meters and / or smart circuit breakers. This operating data includes at least instantaneous power, power generation or consumption, energy storage charge status, and load switching commands, thereby forming a multi-source heterogeneous raw dataset covering the medium and low voltage networks, containing distributed resource operating data and grid status data.

[0085] Subsequently, all raw measurement data were aligned according to a unified sampling period and timestamp. A multi-source fusion algorithm was used to eliminate measurement noise and equipment time deviation, constructing a standardized data sequence for power quality analysis. This standardized data sequence was then mapped to the distribution network topology model, and the common connection point (CCP) between the distribution network and the main grid was located based on node numbers and power flow relationships. At the determined CCP, fundamental positive and negative sequence component analysis was performed on the voltage and current waveforms to obtain the fundamental voltage and current components. Simultaneously, closed-loop dynamic tracking was performed on the voltage and current signals at the CCP, and an adaptive harmonic filtering algorithm was used to dynamically separate the waveforms to obtain the voltage and current harmonic components. Finally, based on the fundamental voltage and current components, voltage harmonic components, and current harmonic components, multiple power quality elements, such as voltage deviation, frequency deviation, three-phase unbalance, and harmonic distortion rate, were quantitatively calculated and comprehensively evaluated to form power quality indices for power quality constraint modeling.

[0086] Furthermore, power quality indicators are set based on the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component. The specific implementation method is as follows:

[0087] Based on the fundamental voltage and current components, a first type of power quality index characterizing the steady-state operation characteristics of the power grid is calculated. A second type of power quality index characterizing the waveform distortion characteristics of the power grid is calculated based on the voltage and current harmonic components. These two types of power quality indices are then integrated to construct a new power quality index. Specifically, based on the fundamental voltage and current components, the steady-state operation characteristics of the point of common coupling (PCC) are quantitatively analyzed, and a first type of power quality index, including voltage deviation, frequency deviation, and three-phase voltage imbalance, is calculated to characterize the voltage, frequency, and phase sequence symmetry levels of distribution network nodes under steady-state conditions. Using the voltage and current harmonic components dynamically separated from the PCC, the waveform distortion of the nodes is evaluated, and a second type of power quality index, including total harmonic distortion rate, harmonic content, and harmonic power and direction, is calculated to characterize the degree of harmonic pollution and flow direction of voltage and current waveforms. After obtaining the above two types of indicators, the first type of power quality indicators and the second type of power quality indicators are integrated according to the preset indicator structure, and a power quality indicator that can comprehensively reflect the steady-state operation characteristics and harmonic distortion characteristics is formed through multi-dimensional quantitative summarization.

[0088] Further, in step 2 of this application embodiment, the power quality constraint vector is constructed according to the power quality indicators to evaluate the distribution network and determine the power quality level. The specific implementation method is as follows: normalization processing is performed based on the power quality indicators to obtain the normalization processing result; the normalization processing result is mapped to a preset interval to determine the initial indicator value; the initial indicator value is linearly weighted to obtain the combined weight coefficient; the initial indicator value is multiplied by the combined weight coefficient to construct a multi-dimensional power quality constraint vector; the distribution network is fuzzy comprehensively evaluated based on the multi-dimensional power quality constraint vector to generate a comprehensive evaluation value; fluctuation penalty is applied based on the comprehensive evaluation value, and the power quality level is determined based on the penalty factor. Specifically, firstly, each power quality indicator is normalized according to a unified standard. The dimensional differences between different indicators are eliminated through interval scaling or extreme value normalization methods, resulting in a normalized evaluation result. Then, the normalized result is mapped to a preset evaluation interval. Based on the mapped initial indicator values, the importance of each indicator in terms of steady-state variation amplitude, harmonic sensitivity, and operational impact is linearly weighted to obtain a combined weight coefficient that simultaneously reflects the subjective importance and objective changes of the indicators. After obtaining the initial indicator values ​​and the combined weight coefficients, they are multiplied dimension-by-dimensionally to construct a multi-dimensional power quality constraint vector containing steady-state power quality elements and fluctuation distortion elements. Based on the multi-dimensional power quality constraint vector, a membership function is constructed, and a fuzzy comprehensive evaluation method is used to comprehensively calculate the current power quality status of the distribution network, generating a comprehensive evaluation value that reflects the overall health level. Finally, a preset fluctuation penalty factor is applied based on the numerical fluctuation of the comprehensive evaluation value to correct the evaluation result. The corrected evaluation value is then mapped to multiple level intervals to determine the power quality level of the distribution network under the current operating conditions.

[0089] Furthermore, the combined weight coefficient is obtained by linearly weighting the initial indicator values. The specific implementation method is as follows: hierarchical analysis is performed based on the initial indicator values ​​to determine the subjective weight vector; entropy weight is calculated based on the initial indicator values ​​to determine the objective weight vector; trigger calculation is performed based on the initial indicator values ​​to obtain the indicator trigger frequency; regulation analysis is performed based on the indicator trigger frequency to obtain the indicator regulation effect; preference learning is performed according to the indicator trigger frequency based on the indicator regulation effect to determine the periodic preference coefficient; the subjective weight vector and the objective weight vector are linearly weighted and fused according to the periodic deviation coefficient to obtain the combined weight coefficient. Specifically, firstly, based on the variation range of each initial indicator value and its impact under different operating scenarios, a pairwise comparison matrix of indicators is constructed using the analytic hierarchy process (AHP) to form a judgment matrix reflecting expert experience and operational strategy preferences. This judgment matrix is ​​then subjected to a consistency check to determine the subjective weight vector characterizing the relative importance of each power quality indicator. Subsequently, based on the time series or horizontal distribution characteristics of the same batch of initial indicator values, the information entropy and entropy weight coefficient of the indicators are calculated using the entropy weight method to obtain an objective weight vector reflecting the objective degree of change and dispersion of the indicators. On this basis, alarms, control commands, and control actions triggered by limit violations, instability, or regulation events of each power quality indicator within historical cycles are statistically analyzed. The trigger frequency of each indicator within the cycle is calculated, and the influence of the indicators in the control chain is analyzed based on the trigger frequency to obtain the indicator control effect characterizing the control effectiveness and sensitivity of each indicator. Based on the indicator control effect and its trigger frequency, a periodic preference coefficient is obtained through a preference learning model to reflect the long-term preference tendency of different indicators in actual operating control scenarios. Finally, the subjective weight vector and the objective weight vector are linearly weighted and fused according to the periodic preference coefficient to obtain a combined weight coefficient.

[0090] Furthermore, in step 3 of this application embodiment, the distributed resources of the distribution network are dynamically aggregated using power quality level as a constraint to form multiple resource clusters. The specific implementation is as follows: The distributed resources are quantified for controllability using power quality level as a constraint, and controllable resource information is extracted; multi-dimensional adjustment analysis is performed based on the controllable resource information to obtain multi-dimensional adjustment response data, which includes active power adjustment response data and reactive power adjustment response data; power quality analysis is performed on the distribution network based on the active power adjustment response data and reactive power adjustment response data to construct a dynamic aggregation index set; distributed resources are dynamically grouped online according to the dynamic aggregation index set to define multiple power quality adjustment data groups; resource matching is performed by traversing multiple power quality adjustment data groups to generate multiple resource clusters. Specifically, firstly, the power quality level is used as a controllable constraint input to perform a controllable quantitative analysis on resources such as distributed photovoltaics, energy storage devices, electric vehicle charging loads, and adjustable industrial loads in the distribution network. Based on the voltage deviation control threshold, reactive power support demand, and harmonic suppression demand corresponding to the power quality level, controllable resource information such as the adjustable active power range, adjustable reactive power range, maximum adjustment rate, and adjustment directionality of each resource under the current operating state is extracted. Subsequently, based on the controllable resource information, multidimensional adjustment analysis is performed on each resource in the active power adjustment dimension and the reactive power adjustment dimension to calculate its adjustment capability when the power quality level constraint conditions are met, forming a multidimensional adjustment response dataset containing active power adjustment response data and reactive power adjustment response data. After obtaining the regulation response data, power quality impact analysis is performed on the voltage support capability, power flow improvement capability, and harmonic suppression capability of distribution network nodes based on the active power regulation response data and reactive power regulation response data. A dynamic aggregation index set that reflects the contribution of resources to power quality improvement is constructed. Based on the dynamic aggregation index set, all distributed resources are dynamically grouped online, and resources with similar regulation capabilities, similar response directions, and similar power quality contribution characteristics are classified into multiple power quality regulation data groups. Finally, resource matching is performed by traversing multiple power quality regulation data groups, so that resource combinations with synergistic regulation effects under the same power quality level constraints are aggregated into independent resource clusters, thereby generating multiple resource clusters for subsequent hierarchical collaborative control.

[0091] Furthermore, in step 4 of this embodiment, limit exceedance analysis is performed based on power quality level to generate hierarchical collaborative control instructions for distributed collaborative regulation of multiple resource clusters. The specific implementation method is as follows: limit exceedance identification is performed based on power quality level to determine multiple dominant limit exceedance indicators; spatiotemporal impact assessment is performed based on the multiple dominant limit exceedance indicators to generate the limit exceedance impact range; multiple adjustment targets are set according to the limit exceedance impact range; multiple hierarchical control architectures are constructed according to the multiple adjustment targets, and these multiple hierarchical control architectures include hierarchical collaborative control instructions; hierarchical collaborative control of multiple resource clusters is performed through the hierarchical collaborative control instructions. Specifically, firstly, the voltage deviation threshold, reactive power support threshold, harmonic distortion limit, and three-phase imbalance limit corresponding to the power quality level are used as over-limit criteria to determine the real-time operating status of the distribution network. When the real-time power quality indicators exceed the limit range corresponding to their level, over-limit identification is performed to determine multiple dominant over-limit indicators that cause power quality anomalies. Subsequently, based on the dominant over-limit indicators, and combined with the distribution network topology, electrical distance, power flow direction, and the time-series response of related nodes, the spatiotemporal impact assessment of the propagation trend of over-limit events in the spatial and temporal dimensions is conducted to quantify the potential area affected by over-limits and form the over-limit impact range. Based on the over-limit impact range, voltage callback targets are set accordingly. The system sets multiple adjustment targets, including reactive power compensation, active power reduction, and harmonic suppression. Based on these targets, a multi-layered control architecture is constructed, encompassing a local control layer, a cluster coordination layer, and a control optimization layer. Within each control layer, corresponding layered collaborative control commands are generated. This enables the local control layer to rapidly adjust nodes exceeding limits, the cluster coordination layer to coordinate and allocate resources across clusters based on their adjustment capabilities, and the control optimization layer to perform global optimal control calculations based on multi-objective and multi-time-domain constraints. Through these layered collaborative control commands, multi-layered collaborative control is implemented across multiple resource clusters, achieving stable suppression of power quality exceedances and restoring the overall operating state of the power grid.

[0092] Furthermore, hierarchical collaborative control of multiple resource clusters is implemented through hierarchical collaborative control commands. Specifically, the hierarchical control architecture includes a local control layer, a cluster coordination layer, and a control optimization layer. Multiple hierarchical control architectures are activated according to the hierarchical collaborative control commands. The local control layer performs local sensing control of the distribution network, generating local power quality data. The local power quality data is synchronized to the cluster coordination layer, which allocates resources to multiple resource clusters according to multiple adjustment targets, generating resource allocation results. The resource allocation results are synchronized to the control optimization layer for multi-scale optimization, generating a general control command to perform global collaborative control and optimization of the distributed resources of the distribution network. Specifically, firstly, by activating the local control layer, it enables rapid local sensing and adjustment of the voltage, current, and harmonic status of the resource nodes based on hierarchical collaborative control commands. This automatically executes control actions such as voltage support, reactive power compensation, load limiting and peak shaving, or local harmonic suppression, and generates real-time local power quality data reflecting changes in the local state of the nodes. Subsequently, the local power quality data is synchronized to the cluster coordination layer. The cluster coordination layer then comprehensively analyzes the adjustment capabilities, directionality, and power quality improvement contributions of different resource clusters based on multiple adjustment targets set during the limit analysis phase, and executes appropriate adjustments accordingly. A cross-cluster resource allocation strategy is adopted to form a resource allocation result for coordinating the joint participation of multiple resource clusters in regulation. Finally, the resource allocation result is synchronized to the control optimization layer, which performs multi-scale optimization on the resource allocation result. Constraint verification and regulation strategy optimization are performed in the short-term, medium-term and long-term operational safety domains respectively. The optimal regulation scheme is determined by a multi-objective, cross-time domain global optimization model, thereby generating a general regulation command for the collaborative work of distributed resources across the entire network. This command is then used to implement global collaborative regulation and optimization of multiple resource clusters in the distribution network, enabling the entire distribution network to achieve stable operation under power quality constraints.

[0093] Furthermore, the resource allocation results are synchronized to the control optimization layer for multi-scale optimization to generate overall control instructions. The specific implementation method is as follows:

[0094] The resource allocation results are synchronized to the control optimization layer for multi-period allocation security analysis. Multiple control security domains are set, including short-term, medium-term, and long-term control security domains. The short-term control security domain is used as a constraint to optimize and control the resource allocation results, obtaining short-term control indicators. The medium-term control security domain is used as a constraint to optimize and control the resource allocation results, obtaining medium-term control indicators. The long-term control security domain is used as a constraint to optimize and control the resource allocation results, obtaining long-term control indicators. The short-term, medium-term, and long-term control indicators are coupled and analyzed according to the time scale to construct the overall control command. Specifically, firstly, the resource allocation results are input into the control optimization layer, and a multi-period safety analysis is performed on their allocation security over future operating cycles. Based on the distribution network operating characteristics, power quality constraints, and load forecasting, short-term, medium-term, and long-term control security domains are set to constrain the feasibility of control strategies at different time scales. Under the short-term control security domain, short-term optimization calculations are performed on the resource allocation results with the goals of rapid voltage recovery, reactive power transient support, local harmonic suppression, and rapid mitigation of node over-limit risks, to obtain short-term control indicators. Using the medium-term control security domain as a constraint, and considering the load fluctuation trend, distributed resource output forecasting, and power quality stability evolution within the cycle, medium-term optimization adjustments are performed on the resource allocation results to obtain medium-term control indicators. Using the long-term control security domain as a constraint, and combining power flow structure evolution, harmonic cumulative effects, resource control fatigue, and multi-cycle operating security, long-term optimization control is performed on the resource allocation results to obtain long-term control indicators. Finally, taking the time scale as the main line, we conduct cross-scale coupling analysis of short-term, medium-term and long-term control indicators. Through weighted fusion, trend consistency verification and multi-scale coordination mechanism, we construct a comprehensive overall control command. This overall control command can guide multiple resource clusters to carry out global coordinated control under different response sequences of block, medium and slow, so as to ensure the stability and optimization of the overall operation of the distribution network under power quality constraints.

[0095] The distributed resource collaborative control method proposed in this application first extracts power quality indicators from the points of common connection by introducing operational data of distributed resources and grid status data in the distribution network. Then, it constructs a power quality constraint vector based on these indicators to evaluate the distribution network and determine its power quality level. Next, using the power quality level as a constraint, it dynamically aggregates the distributed resources of the distribution network to form multiple resource clusters. Finally, it performs limit-crossing analysis based on the power quality level to generate hierarchical collaborative control commands for distributed collaborative control of the multiple resource clusters. This method achieves dynamic aggregation and hierarchical collaborative control of distributed resources under power quality level constraints, significantly improving power quality stability and control reliability. This solves the problem in existing technologies where distributed resource control lacks unified quantitative management of power quality constraints, making it difficult to balance power quality stability in collaborative control.

[0096] Based on the same technical concept described above, this application also proposes a distributed resource collaborative control system based on power quality constraints, such as... Figure 2 As shown, the distributed resource collaborative control system 200 includes:

[0097] The data extraction unit 201 is used to collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling. The specific implementation method is as described in step 1 above, and will not be repeated here.

[0098] Evaluation unit 202 is used to construct a power quality constraint vector based on power quality indicators to evaluate the distribution network and determine the power quality level. The specific implementation method is as described in step 2 above, and will not be repeated here.

[0099] Resource aggregation unit 203 is used to dynamically aggregate distributed resources of the distribution network based on power quality level as a constraint, forming multiple resource clusters. The specific implementation method is as described in step 3 above, and will not be repeated here.

[0100] Additionally, the control unit 204 is used to perform limit-crossing analysis based on power quality levels and generate hierarchical collaborative control commands to perform distributed collaborative control of multiple resource clusters. The specific implementation method is as described in step 4 above and will not be repeated here.

[0101] Based on the same technical concept described above, this application also proposes an electronic device, such as... Figure 3 As shown, the electronic device 300 includes: a memory 310, a processor 320, and a computer program A311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program A311, it performs the following steps:

[0102] Collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling;

[0103] Power quality constraint vectors are constructed based on power quality indicators to evaluate the distribution network and determine the power quality level.

[0104] Using power quality levels as constraints, the distributed resources of the distribution network are dynamically aggregated to form multiple resource clusters;

[0105] Based on power quality levels, limit exceedance analysis is performed to generate hierarchical collaborative control commands for distributed collaborative regulation of multiple resource clusters.

[0106] Optionally, when the processor 320 executes the computer program A311, it can implement any of the corresponding embodiments in the above-described distributed resource collaborative control method.

[0107] It should be noted that the electronic device proposed in this application embodiment is a device used to implement the above-mentioned distributed resource collaborative control method. Therefore, based on the above-mentioned distributed resource collaborative control method proposed in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this application embodiment. Therefore, how the electronic device specifically implements the above-mentioned distributed resource collaborative control method will not be described in detail here. Any electronic device used by those skilled in the art to implement the above-mentioned distributed resource collaborative control method falls within the scope of protection of this application.

[0108] Based on the same technical concept described above, embodiments of this application also propose a computer-readable storage medium, such as... Figure 4 As shown, the computer-readable storage medium 400 stores a computer program B411, which, when executed by a processor, performs the following steps:

[0109] Collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling;

[0110] Power quality constraint vectors are constructed based on power quality indicators to evaluate the distribution network and determine the power quality level.

[0111] Using power quality levels as constraints, the distributed resources of the distribution network are dynamically aggregated to form multiple resource clusters;

[0112] Based on power quality levels, limit exceedance analysis is performed to generate hierarchical collaborative control commands for distributed collaborative regulation of multiple resource clusters.

[0113] Optionally, when the computer program B411 is executed by the processor, it can implement any of the embodiments corresponding to the above-described distributed resource collaborative control method.

[0114] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A distributed resource collaborative regulation method based on power quality constraints, characterized in that, include: Collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling; Based on the power quality indicators, a power quality constraint vector is constructed to evaluate the distribution network and determine the power quality level. Using the power quality level as a constraint, the distributed resources of the distribution network are dynamically aggregated to form multiple resource clusters; Based on the power quality level, limit exceedance analysis is performed, and hierarchical collaborative control commands are generated to perform distributed collaborative regulation of multiple resource clusters.

2. The distributed resource collaborative regulation method based on power quality constraints according to claim 1, characterized in that, The aforementioned method of collecting operational data of distributed resources and grid status data in the distribution network and extracting power quality indicators from the point of common coupling includes: Multi-source heterogeneous raw datasets are obtained by synchronously collecting data from multiple types of intelligent sensing terminals deployed at different levels in the power distribution network; wherein the multi-source heterogeneous raw datasets include operational data and power grid status data. The multi-source heterogeneous original datasets are aligned and fused to construct a standardized data sequence; The standardized data sequence is mapped to the distribution network for location, and the common connection point between the distribution network and the main grid is determined. Based on the common connection point, fundamental wave positive and negative sequence component analysis is performed to extract the fundamental voltage component and the fundamental current component. Closed-loop tracking is performed on the common connection point, and dynamic separation is performed based on the tracking results to obtain voltage harmonic components and current harmonic components. The power quality index is set based on the evaluation of the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component.

3. The distributed resource collaborative regulation method based on power quality constraints according to claim 2, characterized in that, The process of evaluating and setting the power quality index based on the fundamental voltage component, fundamental current component, voltage harmonic component, and current harmonic component includes: Based on the fundamental voltage and fundamental current components, a first type of power quality index characterizing the steady-state operation characteristics of the power grid is calculated. Based on the voltage harmonic components and current harmonic components, a second type of power quality index characterizing the waveform distortion characteristics of the power grid is calculated. The power quality index is constructed by integrating the first type of power quality index and the second type of power quality index.

4. The distributed resource collaborative regulation method based on power quality constraints according to claim 1, characterized in that, The method of constructing a power quality constraint vector based on the power quality indicators to evaluate the distribution network and determine the power quality level includes: The power quality indicators are normalized to obtain the normalized result. The normalization result is mapped to a preset interval to determine the initial index value; The combined weight coefficients are obtained by linearly weighting the initial index values. Multiply the initial index value by the combined weight coefficient to construct a multidimensional power quality constraint vector; Based on the multidimensional power quality constraint vector, a fuzzy comprehensive evaluation of the distribution network is performed to generate a comprehensive evaluation value. Fluctuation penalties are applied based on the comprehensive evaluation value, and the power quality level is determined based on the penalty factor.

5. A distributed resource collaborative regulation method based on power quality constraints according to claim 4, characterized in that, The step of linearly weighting the initial index values ​​to obtain the combined weight coefficients includes: Based on the initial index values, perform hierarchical analysis to determine the subjective weight vector; Entropy weight is calculated based on the initial index values ​​to determine the objective weight vector; Triggering calculations are performed based on the initial indicator values ​​to obtain the indicator triggering frequency; The control effect of the indicators is obtained by analyzing the trigger frequency of the aforementioned indicators. Based on the effect of the indicator regulation, preference learning is performed according to the trigger frequency of the indicator to determine the periodic preference coefficient; The subjective weight vector and the objective weight vector are linearly weighted and fused according to the periodic preference coefficient to obtain the combined weight coefficient.

6. The distributed resource collaborative regulation method based on power quality constraints according to claim 1, characterized in that, The method of dynamically aggregating distributed resources of the distribution network to form multiple resource clusters, using the power quality level as a constraint, includes: The power quality level is used as a constraint to quantify the controllability of distributed resources and extract controllable resource information. Multidimensional regulation analysis is performed based on the controllable resource information to obtain multidimensional regulation response data; wherein the multidimensional regulation response data includes active power regulation response data and reactive power regulation response data. Based on the multidimensional regulation response data, power quality analysis is performed on the distribution network to construct a dynamic aggregated index set. Distributed resources are dynamically grouped online according to the dynamic aggregation index set, and multiple power quality regulation data groups are defined. Multiple power quality regulation data groups are traversed to perform resource matching and generate multiple resource clusters.

7. The distributed resource collaborative regulation method based on power quality constraints according to claim 1, characterized in that, The aforementioned analysis based on the power quality level to generate hierarchical collaborative control commands for distributed collaborative regulation of multiple resource clusters includes: Based on the power quality level, limit violations are identified, and multiple dominant limit violation indicators are determined. Based on multiple dominant over-limit indicators, a spatiotemporal impact assessment is performed to generate the over-limit impact range, and multiple adjustment targets are set according to the over-limit impact range. Multiple hierarchical control architectures are constructed according to the aforementioned adjustment objectives; wherein, the multiple hierarchical control architectures include hierarchical collaborative control instructions; The hierarchical collaborative control instructions are used to perform hierarchical collaborative control on multiple resource clusters.

8. A distributed resource collaborative regulation method based on power quality constraints according to claim 7, characterized in that, The hierarchical collaborative control of multiple resource clusters via the hierarchical collaborative control instructions includes: The aforementioned hierarchical control architecture includes a local control layer, a cluster coordination layer, and a control optimization layer; According to the hierarchical collaborative control command, multiple hierarchical control architectures are activated, and the local control layer performs local sensing control on the distribution network to generate local power quality data. The local power quality data is synchronized to the cluster coordination layer to allocate resources to multiple resource clusters according to multiple adjustment targets, and a resource allocation result is generated. The resource allocation results are synchronized to the control optimization layer for multi-scale optimization, and a general control command is generated to perform global coordinated control and optimization of the distributed resources of the distribution network.

9. A distributed resource collaborative regulation method based on power quality constraints according to claim 8, characterized in that, The process of synchronizing the resource allocation results to the control optimization layer for multi-scale optimization and generating a general control command to perform global coordinated control and optimization of the distributed resources of the distribution network includes: The resource allocation results are synchronized to the control optimization layer for multi-period allocation security analysis to determine multiple control security domains; wherein, the multiple control security domains include short-term control security domains, medium-term control security domains, and long-term control security domains; The short-term control safety domain is used as a limit to optimize and control the resource allocation results, thereby obtaining short-term control indicators; The resource allocation results are optimized and controlled by using the medium-term control security domain as a limit to obtain medium-term control indicators. The long-term control security domain is used as a constraint to optimize and control the resource allocation results, thereby obtaining long-term control indicators. The short-term, medium-term, and long-term control indicators are coupled and analyzed according to time scales to construct the overall control command.

10. A distributed resource collaborative control system based on power quality constraints, characterized in that, include: The data extraction unit is used to collect operational data of distributed resources and power grid status data in the distribution network and extract power quality indicators of the point of common coupling. An evaluation unit is used to construct a power quality constraint vector based on the power quality indicators to evaluate the distribution network and determine the power quality level. The resource aggregation unit is used to dynamically aggregate the distributed resources of the distribution network using the power quality level as a constraint to form multiple resource clusters. In addition, a control unit is used to perform over-limit analysis based on the power quality level and generate hierarchical collaborative control commands to perform distributed collaborative control of multiple resource clusters.

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