Power quality optimization method and system for distribution network with distributed energy access

Abstract

The application relates to the technical field of power quality optimization of power distribution networks, and discloses a power quality optimization method and system for a power distribution network with distributed energy access, which comprises the following steps: collecting distributed energy output and power grid node voltage data in real time, quantifying the incoordination state of the system by calculating fluctuation rate and dispersion; performing preliminary time sequence coordination of multiple units based on the state to determine the output time sequence; obtaining node estimated voltage and evaluating harmonic level by using power flow calculation to trigger the amplitude optimization process; constructing an optimization model with the aim of inhibiting harmonics and stabilizing voltage, fitting the nonlinear relationship between output and voltage / harmonics, generating a constraint set and solving to obtain the final output amplitude sequence; integrating the time sequence and the amplitude sequence to generate a collaborative control instruction. The application effectively suppresses voltage fluctuation and harmonic pollution, and improves the power quality and operation stability of the power distribution network with high-proportion distributed energy access.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power quality optimization of distribution network, in particular to a power quality optimization method and system of distribution network with distributed energy access. BACKGROUND

[0002] With the widespread application of renewable energy, the proportion of distributed energy such as solar energy, wind energy and energy storage systems in low-voltage distribution network is rapidly increasing. This high proportion of access is crucial to energy transformation and sustainable development of power system, because it not only reduces the dependence on traditional fossil energy, but also improves energy utilization efficiency. However, the dispersion and intermittency of distributed energy bring new challenges to the stable operation of low-voltage distribution network, especially in terms of power quality, voltage fluctuation and harmonic pollution, which have become key obstacles to the efficient use of green energy.

[0003] Currently, the solution of distributed energy access to low-voltage distribution network mainly focuses on the independent control of single energy unit, lacking in-depth consideration of the coordinated operation between multiple energy units. This method often cannot effectively deal with the systematic problems caused by the uncoordinated output of each unit when facing high proportion of distributed energy access.

[0004] The disordered output of each energy unit may cause rapid voltage fluctuation in the local power grid, and even cause equipment overload or failure. In addition, the existing solution often ignores the complex interaction of different energy units in output timing and amplitude distribution when dealing with the dynamic characteristics of distributed energy, which makes it difficult to fundamentally alleviate the power quality problem. SUMMARY

[0005] Therefore, the technical problem to be solved by the present application is to overcome the problem of voltage fluctuation and harmonic pollution caused by uncoordinated output of distributed energy in low-voltage distribution network. The present application discloses a power quality optimization method of distribution network with distributed energy access, which can improve the power quality and operation stability of low-voltage distribution network in high proportion of distributed energy scenario.

[0006] To solve the above technical problems, the present application provides a power quality optimization method of distribution network with distributed energy access, comprising the following steps:

[0007] Real-time collection of output timing data of each distributed energy unit in low-voltage distribution network and voltage data of power grid nodes, based on the output timing data, aggregated to generate regional total output curve, and calculation of output fluctuation rate of each unit and dispersion of regional total output;

[0008] Aiming at smoothing the regional total output curve and reducing its fluctuation, a timing coordination optimization model is constructed to obtain a set of preliminary output timing sequence of each unit which minimizes the fluctuation of regional total output;

[0009] Based on the preliminary output time sequence of each unit, power flow calculation is performed to obtain the estimated voltage value of the grid node, the harmonic pollution level under the current operating condition is evaluated, and the amplitude distribution optimization process is triggered;

[0010] In response to the triggering of the amplitude distribution optimization process, a comprehensive target of suppressing harmonic pollution and ensuring node voltage stability is constructed, a nonlinear relationship between distributed energy output and node voltage is fitted, a preliminary amplitude distribution constraint set is generated, and the amplitude distribution optimization model is solved in the constraint set to obtain the final output amplitude sequence of each energy unit;

[0011] The preliminary output time sequence and the final output amplitude sequence are integrated to generate a control instruction sequence sent to each distributed energy unit, and a coordinated instruction for suppressing voltage fluctuation and harmonic pollution is obtained.

[0012] In an embodiment of the present application, the output time sequence data of each distributed energy unit in the low-voltage distribution network and the voltage data of the grid nodes are collected in real time, including the following steps:

[0013] Intelligent monitoring terminals with time synchronization function are deployed at the outlets of each distributed energy unit in the distribution network and at key grid nodes to form a distributed monitoring network;

[0014] The collection parameters of the intelligent monitoring terminal are configured, a unified sampling frequency and data format are set, and a continuous data collection process is started;

[0015] Each intelligent monitoring terminal adds a time stamp and a location identifier to the collected raw data stream to form a standardized data packet, which is uploaded through a hybrid communication network composed of power line carrier and wireless cellular network;

[0016] A data cleaning gateway is set up at the network convergence node to receive the standardized data packet, check it, and remove abnormal values and redundant data caused by signal interference to obtain a clean time sequence data set.

[0017] In an embodiment of the present application, based on the output time sequence data, the output fluctuation rate of each unit and the dispersion of the total regional output are calculated, including the following steps:

[0018] The clean time sequence data set is received, classified by energy unit, and aligned along the time axis to generate independent output change curves of each unit;

[0019] For each independent output change curve, the absolute average value of the output difference between adjacent sampling points in a set calculation time window is calculated, and this value is defined as the real-time output fluctuation rate of the unit;

[0020] Superimpose the independent output change curves of all units to obtain a regional total output curve, calculate the average deviation of the unit output from the average value of the regional total output in the time window, and define the value as the regional output dispersion.

[0021] In an embodiment of the present application, a time sequence coordination optimization model is constructed to smooth the regional total output curve and reduce its fluctuations, and a set of preliminary unit output time sequence sequences that minimize the fluctuations of the regional total output curve is obtained, including the following steps:

[0022] A fluctuation quantification index system of the regional total output curve is established, the fluctuation tolerance interval is determined based on historical data statistical analysis, and the maximum smoothness and the minimum fluctuation extremum of the total output curve are set as dual optimization objectives;

[0023] A multi-energy unit coordination constraint condition library is constructed, including unit output characteristic parameters, operating state limit values and network transmission capacity restrictions, to form a feasible solution space for time sequence coordination;

[0024] A hierarchical optimization architecture is created, which first performs output smoothness self-optimization at the unit level, then performs overall fluctuation coordination at the regional level, and finally performs time sequence sequence verification at the system level;

[0025] A progressive optimization strategy based on perturbation observation is designed, which gradually approaches the optimal coordination scheme by adjusting the unit output time sequence points multiple times with small amplitude and observing the smoothness improvement of the regional total output curve.

[0026] In an embodiment of the present application, it also includes:

[0027] A time sequence stability verification mechanism is developed to test the anti-interference ability of the preliminary output time sequence sequence obtained by optimization, and to ensure that the coordination effect is maintained within the expected fluctuation range;

[0028] The preliminary output time sequence sequence that has passed the stability verification is output, and the optimal output time point and power change gradient of each unit are recorded to provide a basic time sequence framework for subsequent amplitude allocation.

[0029] In an embodiment of the present application, based on the preliminary unit output time sequence sequence, power flow calculation is performed to obtain the estimated voltage value of the grid node, the harmonic pollution level under the current operating condition is evaluated, and the amplitude allocation optimization process is triggered, including the following steps:

[0030] A power grid operation scenario simulator based on the preliminary output time sequence sequence is established, and the unit output is mapped to the distribution network topology model according to the time sequence;

[0031] Multi-time scale rolling power flow calculation is performed, starting from the current time and calculating the voltage change trajectory of each grid node in a certain period of time in a progressive time window manner;

[0032] A voltage quality comprehensive evaluation matrix is constructed to comprehensively analyze voltage deviation, fluctuation amplitude and duration of each node, identify voltage out-of-limit risk period and position;

[0033] Synchronous harmonic pollution situation assessment is performed, and by analyzing the correlation between the output characteristics of each unit and the harmonic emission characteristics, the harmonic distribution under different operation scenarios is predicted;

[0034] A voltage harmonic collaborative evaluation mechanism is established, and when the spatial and temporal correlation between voltage quality deterioration and harmonic pollution aggravation is monitored, an amplitude allocation optimization trigger signal is generated;

[0035] An adaptive adjustment strategy of the optimization trigger threshold is designed, and the trigger condition is dynamically adjusted according to the grid operation state, so that the amplitude allocation optimization can be accurately started when necessary.

[0036] In an embodiment of the present application, in response to the triggering of the amplitude allocation optimization process, a nonlinear relationship between the distributed energy output and the node voltage is fitted to suppress harmonic pollution and ensure node voltage stability, and a preliminary amplitude allocation constraint set is generated, including the following steps:

[0037] A multi-objective optimization weight distribution mechanism is established, and the priority weights of harmonic suppression and voltage stability are dynamically adjusted according to the real-time grid state to form a differentiated optimization strategy;

[0038] A distributed energy output-node voltage response feature library is constructed, and the influence of output changes of each type of energy unit on node voltage is mined through historical operation data;

[0039] A harmonic pollution correlation analysis model is designed, and a corresponding relationship diagram between harmonic emission characteristics and voltage distortion degree under different output conditions is established;

[0040] A constraint condition adaptive generation algorithm is developed, and based on the real-time grid topology and operation state, the boundary constraint conditions of amplitude allocation are dynamically generated;

[0041] A multi-dimensional constraint coordination mechanism is created to coordinate the conflict relationship among equipment operation limits, network transmission capacity, power quality indicators and other types of constraint conditions;

[0042] A hierarchical constraint set is generated, and the constraint conditions are classified according to the emergency degree and the influence range to form a preliminary amplitude allocation constraint set that can satisfy the key constraints in priority.

[0043] In an embodiment of the present application, the amplitude allocation optimization model is solved in the constraint set to obtain the final output amplitude sequence of each energy unit, including the following steps:

[0044] A solving process monitoring mechanism is established to monitor the optimization solving progress and constraint satisfaction in real time, and dynamically adjust the solving strategy;

[0045] A multi-stage progressive solving strategy is designed to first quickly obtain a feasible solution, and then gradually optimize the objective function based on the feasible solution;

[0046] A constraint violation degree evaluation system is developed to quantitatively evaluate the constraint compliance of the temporary solution generated in the solving process;

[0047] A solution quality comprehensive evaluation model is constructed to evaluate the quality of the solution from multiple dimensions such as the objective function value, constraint satisfaction degree, and implementation feasibility;

[0048] A solving termination adaptive judgment mechanism is designed to intelligently judge the optimal termination time according to the solving progress and solution quality change trend;

[0049] The final output amplitude sequence after comprehensive evaluation is output, and the power regulation range and rate limit of each unit are recorded to provide complete amplitude information for control instruction generation.

[0050] In an embodiment of the present application, the preliminary output time sequence and the final output amplitude sequence are integrated to generate a control instruction sequence sent to each distributed energy unit, obtaining a coordinated instruction for suppressing voltage fluctuation and harmonic pollution, including the following steps:

[0051] The time coordinates of the preliminary output time sequence and the power values of the final output amplitude sequence are fused and matched to generate a basic instruction set containing time-power two-dimensional attributes;

[0052] The basic instruction set is smoothed and safety checked to ensure the continuity of instruction change and the safety of device operation;

[0053] The standardized control instructions are packaged according to the predetermined communication protocol and distributed to each distributed energy unit for execution.

[0054] To solve the above technical problems, the present application also discloses a power quality optimization system for a power distribution network with distributed energy access, comprising:

[0055] A data acquisition and state evaluation module is used to acquire output time sequence data of each distributed energy unit and voltage data of the power grid node in the low-voltage power distribution network in real time, generate a regional total output curve based on the output time sequence data, and calculate the output fluctuation rate of each unit and the dispersion of the regional total output;

[0056] A time sequence coordination optimization module is used to construct a time sequence coordination optimization model to smooth the regional total output curve and reduce its fluctuation, and obtain a set of preliminary output time sequence of each unit that minimizes the regional total output fluctuation;

[0057] a harmonic evaluation and triggering module, configured to perform power flow calculation based on the preliminary output time sequence of each unit, to obtain estimated voltage values of grid nodes, to evaluate the harmonic pollution level under the current operating condition, and to trigger an amplitude distribution optimization process;

[0058] an amplitude distribution optimization module, configured to, in response to the triggering of the amplitude distribution optimization process, construct an amplitude distribution optimization model, to fit the nonlinear relationship between the distributed energy output and the node voltage, to generate a preliminary amplitude distribution constraint set, to solve the amplitude distribution optimization model within the constraint set, and to obtain a final output amplitude sequence of each energy unit, with the comprehensive goal of suppressing harmonic pollution and ensuring node voltage stability;

[0059] an instruction generation and execution module, configured to integrate the preliminary output time sequence and the final output amplitude sequence, to generate a control instruction sequence sent to each distributed energy unit, and to obtain a coordinated instruction for suppressing voltage fluctuation and harmonic pollution.

[0060] The above technical solutions of the present application have the following advantages compared with the prior art:

[0061] The present application collects distributed energy output and grid voltage data in real time, extracts intermittent and dispersed characteristics, accurately identifies output uncoordination state, performs preliminary time sequence coordination first to generate an output time sequence of each energy unit, then calculates the estimated voltage values of grid nodes for evaluating the harmonic pollution level under the current operating condition, and triggers the amplitude distribution optimization process. The amplitude distribution optimization model is constructed, the distribution constraint set is determined, and the final output amplitude sequence obtained by solving the model within the constraint set is an optimal solution that meets the time sequence smoothness requirement and ensures voltage stability and harmonic compliance, forming a complete multi-unit coordinated output plan.

[0062] The present application ensures effective execution of the coordinated instruction through data distribution and voltage stability judgment, significantly suppresses voltage fluctuation and harmonic pollution, and improves the power quality of low-voltage distribution networks, providing an efficient and stable optimization scheme for high-proportion distributed energy access. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the accompanying drawings, in which:

[0064] Figure 1 is a step flow chart of the power quality optimization method of the distribution network of the present application for distributed energy access;

[0065] Figure 2 is a step flow chart of data collection of the present application;

[0066] Figure 3is a step flow chart of the output fluctuation rate and dispersion calculation of the present application;

[0067] Figure 4 is a step flow chart of the timing coordination optimization model construction and solution of the present application;

[0068] Figure 5 is a step flow chart of the harmonic evaluation and trigger judgment of the present application;

[0069] Figure 6 is a step flow chart of the amplitude distribution constraint set generation of the present application;

[0070] Figure 7 is a step flow chart of the amplitude distribution optimization model solution of the present application;

[0071] Figure 8 is a step flow chart of the control instruction generation and execution of the present application;

[0072] Figure 9 is a structural framework diagram of the power quality optimization system of the distribution network with distributed energy access of the present application. DETAILED DESCRIPTION

[0073] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0074] The core technical difficulty of distributed energy access is how to realize the coordinated cooperation between multiple energy units. Since the output of distributed energy has high randomness and volatility, the output timing of each unit in the time dimension is difficult to unify. For example, solar power generation is affected by the weather, and the output may change dramatically in a short time, while the response speed and capacity distribution of the energy storage system are difficult to accurately match such rapid changes. This uncoordination in timing directly leads to the intensification of voltage fluctuation. In addition, the imbalance in output amplitude distribution of each energy unit further aggravates the problem; for example, when multiple distributed power sources in a certain area are simultaneously operated at high load, it may cause the voltage of the local power grid to exceed the safe range, while at other times it may cause voltage to drop due to insufficient output. This uncoordination in timing and amplitude poses a serious threat to the stability of the power grid.

[0075] Therefore, how to coordinate the output timing and amplitude distribution of multiple energy units in the scenario of high proportion of distributed energy access to effectively suppress voltage fluctuation and harmonic pollution has become a key problem to ensure the power quality of low-voltage distribution networks. The solution to this problem not only needs to consider the dynamic characteristics of each energy unit, but also needs to build a collaborative operation mechanism that can adapt to complex grid environments to achieve smooth interaction between distributed energy and low-voltage distribution networks.

[0076] Based on the above analysis, referring to Figure 1 Based on the above analysis, referring to Figure 1 The present application provides a power quality optimization method for a power distribution network with distributed energy access, which constructs a full-process closed-loop control system running through data collection and state evaluation, time sequence coordination optimization, time sequence scheme verification and harmonic evaluation, amplitude distribution optimization, and generation and execution of coordinated instructions, including the following steps:

[0077] Real-time collection of output time sequence data of each distributed energy unit in the low-voltage power distribution network and voltage data of the grid nodes, based on the output time sequence data, aggregation to generate a regional total output curve, and calculation of the output fluctuation rate of each unit and the dispersion of the regional total output;

[0078] The method first collects and quantitatively evaluates the real-time running state data of the power grid, converts the fuzzy uncoordinated state into accurate mathematical indicators by calculating the output fluctuation rate of each unit and the dispersion of the regional total output, and provides reliable decision basis for subsequent optimization, avoiding the blindness of control.

[0079] A time sequence coordination optimization model is constructed to smooth the regional total output curve and reduce its fluctuation, and a set of preliminary output time sequence sequences of each unit that minimizes the fluctuation of the regional total output is obtained;

[0080] Further, the method is not simply mechanically superimposed with time sequence and amplitude optimization, but first focuses on solving the time sequence matching problem of output, and through the construction of an optimization model with the goal of smoothing the regional total output curve, the method is committed to suppressing the overall power fluctuation caused by the intermittency of wind and light resources from the source, thereby preliminarily stabilizing the voltage baseline of the power grid.

[0081] Based on the preliminary output time sequence sequence, power flow calculation is performed to obtain the estimated voltage value of the grid nodes, and the harmonic pollution level under the current operating condition is evaluated to trigger the amplitude distribution optimization process;

[0082] Further, the present application introduces a "verification-triggering" mechanism, and introduces power flow calculation as a key verification link. This step simulates and deduces the preliminary time sequence plan in the real physical model of the power distribution network to accurately obtain the estimated voltage value of the grid nodes, thereby exposing the voltage out-of-limit problem that the pure time sequence scheme may cause in advance, and simultaneously evaluating the harmonic pollution level. This step skillfully communicates the power generation plan and the grid state, and is an intelligent decision point for triggering the subsequent advanced optimization process; when the problem is predicted, the system will "trigger the amplitude distribution optimization process", which marks the transition of the scheme from the pure time sequence dimension to the new stage of time sequence-amplitude coordinated optimization.

[0083] In response to the triggering of the amplitude allocation optimization process, an amplitude allocation optimization model is constructed to suppress harmonic pollution and ensure node voltage stability as the comprehensive goal, a nonlinear relationship between distributed energy output and node voltage is fitted, a preliminary amplitude allocation constraint set is generated, the amplitude allocation optimization model is solved in the constraint set to obtain a final output amplitude sequence of each energy unit;

[0084] In the amplitude optimization stage, instead of simply and roughly adjusting power, an amplitude allocation optimization model is constructed, and particular attention is paid to fitting the nonlinear relationship between distributed energy output and node voltage / harmonic, which reflects the respect for the complex physical laws of the power grid. The complex interaction between variables is described and constrained through advanced algorithms, so as to generate a preliminary amplitude allocation constraint set, ensuring the technical feasibility of the optimization direction. Finally, the final output amplitude sequence obtained by solving the model in the constraint set is an optimal solution that meets the timing smoothness requirement and ensures voltage stability and harmonic compliance.

[0085] The preliminary output timing sequence and the final output amplitude sequence are integrated to generate a control instruction sequence sent to each distributed energy unit, obtaining a coordinated instruction for suppressing voltage fluctuation and harmonic pollution.

[0086] Finally, by integrating the preliminary output timing sequence and the final output amplitude sequence, a unique set of coordinated control instructions is generated, which contains information in both time and power dimensions, realizing fine and global coordinated control of distributed energy units.

[0087] From the principle, the beneficial effects of the cooperative work of these technical features are obvious: through the double closed-loop optimization of timing and amplitude, the scheme can fundamentally suppress voltage fluctuation caused by random fluctuation of distributed energy, and effectively reduce the risk of harmonic pollution by actively considering the harmonic level in the model; the whole method follows the logic chain of "evaluation-preliminary optimization-simulation verification-coordinated optimization", its response speed and decision-making scientificity are much higher than those of traditional centralized scheduling, and its global optimization capability is much higher than that of local control, finally significantly improving the power quality and operation stability of low-voltage distribution networks in high-proportion distributed energy scenarios.

[0088] Specifically, in the data collection process, there are many distributed collection points. If the terminal clocks are not synchronized and the data formats are different, the data cannot be accurately aligned on the time axis, losing the significance of comparative analysis. Traditional single communication mode is prone to blind spots in complex distribution environments, resulting in data packet loss.

[0089] To solve the above problems, with reference to Figure 2As shown, the application further discloses a data collection method, comprising: deploying intelligent monitoring terminals with time synchronization function at the outlets of each distributed energy unit of the power distribution network and at key grid nodes to form a globally covered distributed monitoring network; this deployment strategy ensures that the source of data collection is directly pointed to all control objects (energy units) that need to be coordinated and system state indicators (node voltage) that need to be monitored, providing a comprehensive data view for subsequent collaborative control.

[0090] Further, by configuring all intelligent monitoring terminals with a unified sampling frequency and data format and starting continuous collection, this scheme institutionally ensures that all monitoring point data is generated at the same time scale, effectively avoiding subsequent state evaluation distortion caused by asynchronous data, and laying a time sequence consistency foundation for accurate quantification of uncoordinated state. The collected raw data stream is added with accurate time stamp and unique location identifier to form a standardized data packet, which gives the massive data clear spatiotemporal attributes, enabling accurate alignment and correlation of data from different devices within a wide geographical range, which is a prerequisite for analyzing the dispersion and interaction between units.

[0091] Subsequently, these information-rich data packets are uploaded through a hybrid communication network composed of power line carrier and wireless cellular network; this heterogeneous network design combines the wide coverage of power line carrier communication and the flexibility and reliability of wireless cellular network, ensuring the robustness of data transmission channels in complex power distribution environments and guaranteeing the continuity and integrity of optimization system input data.

[0092] Finally, the data cleaning gateway set up at the network convergence node assumes the role of "quality gatekeeper", which checks the received standardized data packets and intelligently removes abnormal values and redundant data caused by signal interference, thereby outputting a clean time sequence data set.

[0093] The technical effect of this whole process is that it converts the dispersed, raw, and possibly noisy electrical signals in the physical world into a clean data set with high precision, high reliability, and strict spatiotemporal alignment in the digital world. This data directly serves the subsequent steps of the application - calculating volatility, dispersion, and performing power flow calculation. The mechanism is that only based on such a true, consistent, and clean data, the evaluated uncoordinated state is accurate, the constructed optimization model conforms to the actual physical situation of the power grid, and the generated preliminary output time sequence and final output amplitude sequence are executable, thereby truly achieving the optimization goal of suppressing voltage fluctuation and harmonic pollution.

[0094] Specifically, based on the above output timing data, the output fluctuation rate of each unit and the dispersion of the total regional output are calculated, and the purpose is to convert the obtained clean timing data set into two quantitative indicators with clear physical meaning, namely the output fluctuation rate and the dispersion of the regional output, so as to provide a core criterion for the entire optimization system to diagnose the "uncoordinated state" of the power grid, as shown in Figure 3 includes the following steps:

[0095] The deep structured processing of the data set, that is, the classification according to the energy units and the strict alignment along the time axis to generate the independent output change curve of each unit, ensures that the individual behavior characteristics of each distributed power supply are clearly and independently captured and displayed, laying a solid foundation for subsequent detailed analysis.

[0096] On this basis, for each independent curve, the algorithm calculates the absolute average value of the output difference of adjacent sampling points within a set calculation time window, and defines this value as the real-time output fluctuation rate of the unit, that is, for a distributed energy unit i, within a set calculation time window containing N sampling points, the output sequence is Pi(1), Pi(2), …, Pi(N), the average value of the absolute difference of the output in N-1 continuous time intervals in the sequence is calculated, and the value is defined as the real-time output fluctuation rate Ri of the unit, and the calculation formula is:

[0097] ;

[0098] Where N is the total number of sampling points in the calculation time window, t represents the t-th sampling time in the time window, and the output sequence is Pi(1), Pi(2), …, Pi(N);

[0099] The deep mechanism of this calculation strategy is that it skillfully avoids the information ambiguity problem that may be caused by simply using variance or range, and instead directly describes the intensity and frequency of output change within a unit of time; a higher fluctuation rate value reveals that the unit (such as a photovoltaic panel affected by a rapidly moving cloud) is in a state of strong randomness and instability, and its output is undergoing short and violent jumps, which is a direct cause of voltage flicker and fluctuation.

[0100] At the same time, in order to evaluate the coordination from the overall system level, the total regional output curve is obtained by superimposing all independent curves, and the average deviation of the output of each unit from the average value of the total regional output within the same time window is further calculated, that is, the dispersion of the regional output, wherein:

[0101] The calculation formula of the total regional output is:

[0102] ;

[0103] The calculation formula of the regional average output is:

[0104]

[0105] The calculation formula of the regional output dispersion is:

[0106]

[0107] wherein, the output sequence is Pi(t), t = 1, 2, …, N, which represents the output of unit i at time t, N represents the number of sampling points, and M represents the total number of distributed energy units in the region.

[0108] The design principle of the index is to measure the synchronization and consistency of the behaviors of different units in response to the same external environment or dispatching instruction; a higher dispersion means that some units are running much higher than the average output (which may cause local overvoltage), while others are much lower than the average (which may cause voltage sag), and this abnormal running state of dispersion is the root cause of uneven power flow distribution, increased line loss and deteriorated voltage quality in the distribution network.

[0109] Therefore, through the above two calculation steps, the technical effect is to provide indispensable and quantitative decision inputs for subsequent collaborative optimization. The volatility index accurately locates the “unstable source” that needs to be prioritized for smoothing, and the dispersion index macroscopically determines the mismatch degree of the overall system operation. Based on the two accurate and quantitative indexes, the subsequent can have a clear target to reduce the total regional fluctuation and reduce the dispersion, and construct an effective time sequence coordination optimization model, thereby fundamentally starting the accurate and efficient governance of power quality problems, and the entire evaluation process realizes the sublimation from raw data to decision knowledge, which is the key link between the upper and lower in the intelligent optimization control closed loop.

[0110] Referring to Figure 4 The present application further extends the time sequence coordination optimization process in depth and systematically, and constructs a complete multi-level optimization framework, including: establishing a volatility quantification index system of the regional total output curve, this step scientifically determines the tolerance interval of the fluctuation based on the statistical analysis of the historical operation data, and clearly sets the maximum smoothing degree of the total output curve and the minimum fluctuation extreme value as the dual optimization objectives; the technical effect is to provide accurate and quantifiable evaluation criteria for the entire optimization process, so that the two macro objectives of “smoothing” and “reducing fluctuation” are transformed into specific mathematical pursuits, avoiding the ambiguity of the optimization direction, which is a prerequisite for accurate coordination.

[0111] Specifically, the dual optimization objectives are constructed as the minimization problem of the following objective function F1:

[0112] ​​ ;

[0113] Wherein: a and β are weight coefficients and satisfy a+β=1, for balancing the weight of smoothness and fluctuation extreme value; Ptotal(t) is the total area output at t time; N is the total sampling point number in the optimization period.

[0114] On the basis of clear objectives, by constructing a multi-energy unit coordination constraint condition library, the inherent output characteristics parameters of each unit (such as maximum and minimum output, ramp rate), real-time operating state limit and key network transmission capacity restriction are all taken into account, thereby clearly defining the feasible solution space of time sequence coordination; the technical mechanism of this step is to ensure that any time sequence generated by subsequent optimization strictly follows the physical laws and safety criteria of power grid operation, preventing the generation of unfeasible or dangerous control instructions for the pursuit of smoothness from the source, and ensuring the practicality and safety of the optimization scheme.

[0115] In order to efficiently optimize in this high-dimensional, multi-constrained solution space, a hierarchical optimization architecture is further created, and the optimization logic is not achieved at one stroke, but first at the unit level, each distributed energy source performs preliminary self-optimization of output smoothness based on its own characteristics, then rises to the regional level to coordinate the overall fluctuation to balance the contradictions between units, and finally verifies the generated time sequence at the system level; this progressive process from bottom to top and from local to global greatly reduces the complexity of the optimization problem, avoids the "curse of dimensionality" problem that may be caused by direct system-level optimization, and takes into account both individual characteristics and overall system objectives, making the optimization process more efficient and easy to converge.

[0116] Finally, a progressive optimization strategy based on disturbance observation is designed, which does not rely on complex mathematical models and global search, but intelligently adjusts each unit output time sequence point multiple times with small amplitude, and observes the improvement of the smoothness of the total area output curve in real time, to determine whether the adjustment direction is correct and gradually approach the optimal coordination scheme.

[0117] The mechanism of this embodiment is similar to "crossing the river by feeling the stones", and its technical advantage lies in strong robustness, low requirement for model accuracy, good adaptation to the uncertainty of distributed energy output, and stable optimization process ensured by continuous small step iteration, finally stably outputting a set of preliminary output time sequence of each unit that minimizes the fluctuation of the total area output, laying a solid and reliable time sequence foundation for subsequent amplitude allocation optimization.

[0118] Further, in order to ensure that the preliminary timing sequence generated by the above steps is not only theoretically optimal, but also robust in the actual power grid environment and provides a structured input for subsequent amplitude allocation optimization, the embodiment further adds the crucial stability check and output normalization link based on the constructed timing coordination optimization framework, further comprising: a set of timing sequence stability check mechanisms is developed, the core of which is to test the anti-interference ability of the preliminary output timing sequence obtained by optimization; the mechanism of this step is to simulate various expected fluctuations that may occur in actual operation (such as temporary output decline or surge of a single unit), to verify whether the regional total output curve can still remain smooth and the fluctuation extreme value can still be maintained within the tolerance interval under the sequence control, and its technical effect is to expose the potential vulnerability of the optimization scheme in the dynamic environment in advance, avoiding the direct application of a "fragile" sequence generated under ideal conditions, thereby significantly improving the reliability of the entire coordinated control system.

[0119] The preliminary output timing sequence that passes the stability check is allowed to be output, and at the same time, the key control feature information contained in the sequence, i.e. the optimal output time point and power change gradient of each unit, is recorded simultaneously; recording the optimal output time point essentially determines the core responsibility time of each unit under the coordination framework, and recording the power change gradient restricts the rate of power regulation, and the two together constitute an accurate "basic timing framework". The technical mechanism is that the subsequent amplitude allocation optimization is not carried out in a vacuum, it must strictly follow the coordinated timing framework, otherwise it will destroy the timing coordination effect already achieved. The basic timing framework defines the operation boundary (i.e. amplitude adjustment within a certain time point and its neighborhood) for amplitude optimization and provides a reference for the adjustment rate, ensuring that timing and amplitude optimization can seamlessly connect and work together, rather than conflict with each other. Therefore, the two added steps have an overall technical effect of continuity: it serves as the end point of the timing coordination optimization process, providing quality assurance for the practicality of its results through stability check; it also serves as the starting point of the subsequent amplitude allocation optimization process, providing clear constraints and inputs through the output of the structured basic timing framework, ultimately ensuring the coherence, synergy and reliability of the entire multi-level optimization process.

[0120] Reference Figure 5As shown, the application further extends the timing scheme verification and harmonic evaluation process in depth and systematically, and builds a complete power grid operation state prediction and evaluation triggering mechanism, including: establishing a power grid operation scenario simulator based on the preliminary output timing sequence, by accurately mapping the planned output of each unit to the distribution network topology model, realizing the digital mapping from control instructions to power grid physical response, providing a real simulation environment for subsequent analysis. On this basis, multi-time scale rolling load flow calculation is performed, and the voltage change trajectory of each time window node is calculated in a forward-looking manner. This dynamic calculation method can accurately capture the voltage transient process that may occur during the execution of the timing sequence, providing continuous time dimension data for risk assessment.

[0121] Based on the results of the load flow calculation, a voltage quality comprehensive evaluation matrix is constructed to quantitatively analyze the voltage state of each node from three dimensions of voltage deviation, fluctuation amplitude and duration. This multi-index fusion evaluation method can accurately identify the risk level, occurrence period and specific location of voltage out-of-limit, overcoming the limitations of single index judgment. The synchronous harmonic pollution situation assessment predicts the distribution of harmonic current and the content of harmonic voltage under different operating scenarios by establishing a correlation model between unit output characteristics and harmonic emission characteristics. This assessment can identify the harmonic resonance risk in areas with dense access of power electronic equipment.

[0122] Further, a voltage and harmonic co-evaluation mechanism is established. When voltage quality deterioration and harmonic pollution intensification show correlation in time and space, it indicates that the system has a composite power quality problem that requires amplitude coordination to solve. At this time, an amplitude allocation optimization trigger signal is generated.

[0123] Finally, by designing an optimization trigger threshold adaptive adjustment strategy, the trigger condition is dynamically adjusted according to the real-time operation state of the power grid, ensuring that the amplitude allocation optimization neither starts too early causing unnecessary computational overhead, nor starts too late missing the best control opportunity. This embodiment realizes precise judgment and timely response of the amplitude allocation optimization demand through layer-by-layer progressive analysis and evaluation, providing a reliable decision basis for the subsequent optimization link, thereby ensuring the effectiveness and economy of the coordinated control system.

[0124] Reference Figure 6As shown, the application further expands the step of amplitude allocation optimization, and establishes a complete constraint set generation mechanism, including: establishing a multi-objective optimization weight allocation mechanism, dynamically adjusting the priority weight of harmonic suppression and voltage stability according to the real-time power grid state, this step can flexibly adjust the optimization direction according to the actual demand of the power grid, when the harmonic pollution is serious, the harmonic is suppressed first, when the voltage stability is poor, the voltage stability is guaranteed first, forming a differentiated optimization strategy, ensuring that the optimization target is highly consistent with the actual demand, and the specific amplitude allocation optimization objective function can be quantified as the minimization problem of the following objective function F2:

[0125] ;

[0126] Wherein: λ1 and λ2 are dynamic adjustment priority weight coefficients, and satisfy λ1+λ2=1; Vj is the estimated voltage value of node j, Vref is the rated reference voltage; THDvj is the estimated voltage total harmonic distortion rate of node j; M is the number of key monitoring nodes.

[0127] On this basis, by constructing a distributed energy output-node voltage response feature library, the influence law of the output change of each type of energy unit on the node voltage is mined from the historical operation data, this step establishes the quantitative relationship between the unit output and the grid state, providing an important input-output mapping relationship for subsequent optimization, making the optimization model more close to the actual physical characteristics.

[0128] Synchronously, a harmonic pollution correlation analysis model is designed, which establishes a detailed harmonic distribution atlas by analyzing the corresponding relationship between harmonic emission characteristics and voltage distortion degree under different output conditions, this step can accurately predict the harmonic problems that may be caused by a specific output scheme, providing forward-looking guidance for harmonic suppression. Based on the above analysis results, a constraint condition adaptive generation algorithm is developed, which dynamically generates the boundary constraint conditions of amplitude allocation according to the real-time power grid topology and operating state, this step ensures that the constraint conditions can be adaptively adjusted with the change of the power grid operating mode, improving the adaptability and accuracy of the optimization model. Subsequently, a multi-dimensional constraint coordination mechanism is created, which specially handles the conflict relationship between multiple types of constraint conditions such as device operating limits, network transmission capacity, power quality indicators, etc., by establishing constraint priority and coordination rules, effectively solving the contradiction problems that may exist between different constraint conditions.

[0129] Finally, by generating a hierarchical constraint set, the constraint conditions are classified according to the urgency and influence range, forming a preliminary amplitude allocation constraint set that can satisfy the key constraints first, this step ensures that the most important safety and stability constraints are satisfied first, and at the same time provides a structured constraint condition input for subsequent optimization solution.

[0130] The embodiment ensures that the amplitude distribution optimization model can fully consider various constraint conditions of actual operation of the power grid and can be dynamically adjusted according to real-time states, lays a solid foundation for generating a safe, reliable and efficient amplitude distribution scheme, and thus guarantees the effectiveness and reliability of the final coordinated control effect.

[0131] Referring to Figure 7 As shown in the figure, on the basis of the established optimization model and constraint set, in order to further improve the solving process of the amplitude distribution optimization model, the application also constructs an intelligent solving quality control system, including: first, a solving process monitoring mechanism is established to monitor the progress of optimization solving and the satisfaction of each constraint condition in real time. This step can timely find problems such as oscillation, stagnation or divergence that may occur in the solving process, provide decision basis for dynamically adjusting the solving strategy, and ensure that the solving process always advances in the correct direction. Based on the monitoring feedback, a multi-stage progressive solving strategy is designed, which first takes obtaining a feasible solution that satisfies all constraint conditions as the primary goal to ensure the basic feasibility of the solution, and then gradually optimizes the objective function based on the feasible solution. This step-by-step approximation method ensures the quality of the solution and effectively improves the solving efficiency.

[0132] To further ensure the quality of the solution, a constraint violation degree evaluation system is developed to quantitatively evaluate the constraint compliance of each temporary solution generated in the solving process. This step can objectively evaluate the degree of deviation of each solution from the constraint condition by establishing an accurate violation degree measurement standard, providing quantitative guidance for adjusting the solving direction. At the same time, a solution quality comprehensive evaluation model is constructed to comprehensively evaluate the solution from multiple dimensions such as objective function value, constraint satisfaction degree and implementation feasibility. This comprehensive evaluation mechanism avoids the one-sidedness of single indicator evaluation, and ensures that the final obtained solution achieves the best balance between theoretical performance and engineering practice.

[0133] Further, a solving termination adaptive judgment mechanism is designed to intelligently judge the optimal termination time according to the change trend of the solving progress and the solution quality. This step can effectively avoid the waste of computing resources caused by premature termination or late termination, and achieve the optimal trade-off between solving efficiency and solution quality. Finally, the final output amplitude sequence that has been comprehensively evaluated is output, and key parameters such as power regulation range and speed limit of each unit are recorded. This step not only provides a specific amplitude distribution scheme, but also provides complete amplitude information support for the generation of subsequent control instructions, ensuring that the control instructions meet the optimization target and comply with the actual operation limit of the equipment.

[0134] The embodiment establishes a complete solution quality guarantee system, ensures that the amplitude distribution optimization process can efficiently and reliably obtain a high-quality solution, and provides a solid guarantee for finally achieving the power quality optimization target.

[0135] Referring to Figure 8 As shown in the figure, the application further extends the final instruction generation link, and builds an efficient and reliable control instruction synthesis and issuing process, including: accurately fusing and matching the time coordinates of the preliminary output time sequence and the power values of the final output amplitude sequence, this step organically integrates the time sequence arrangement and power allocation obtained by the previous optimization link through the establishment of a two-dimensional mapping relationship between time and power, generates a basic instruction set containing complete control information, and the technical effect lies in ensuring that each distributed energy unit is allocated with an accurate output value at a specific time point, ensuring the coordination and unity of the time dimension and the power dimension from the source of the instruction, which is the basis for effective coordinated control.

[0136] On this basis, the basic instruction set is smoothed, the change rate of power instructions at adjacent time points in the instruction sequence is limited, the power instruction mutation points are eliminated, and a smooth and continuous power change trajectory is generated, the mechanism of this processing process lies in avoiding the impact of power instruction jump on power electronic converters and other equipment, preventing new power quality problems caused by equipment response characteristics, and enhancing the executability of the instruction. The synchronous safety check reviews the instruction sequence from multiple dimensions such as equipment operating limits and network transmission capacity, to ensure that each instruction is within the safe operating range, and the technical effect lies in preventing idealized instructions generated by the optimization algorithm from exceeding the actual operating capacity of the equipment, causing equipment damage or system instability, and ensuring the safety and reliability of the entire coordinated control process.

[0137] Finally, the instructions that pass the check are packaged into standardized control instructions according to the predetermined communication protocol, and distributed to each distributed energy unit for execution, this step ensures the accuracy and reliability of the control instructions in the transmission process by using a unified communication protocol and data format, the technical mechanism lies in avoiding control deviation caused by communication misunderstanding or data errors, and through an efficient instruction distribution mechanism, all units can receive and execute instructions synchronously, finally all the optimization calculation results can be accurately converted into actual control actions, and the coordinated control goal of suppressing voltage fluctuation and harmonic pollution is achieved.

[0138] Referring to Figure 9 In order to realize the above method, the application further discloses a power quality optimization system of a distribution network with distributed energy access, comprising:

[0139] A data acquisition and state evaluation module is used to acquire output time sequence data of each distributed energy unit in a low-voltage distribution network and voltage data of a power grid node in real time, based on the output time sequence data, a regional total output curve is generated by aggregation, and the output fluctuation rate of each unit and the dispersion of the regional total output are calculated.

[0140] a timing coordination optimization module, configured to construct a timing coordination optimization model for the purpose of smoothing the total regional output curve and reducing its fluctuation, and obtain a set of preliminary output timing sequences of each unit that minimize the fluctuation of the total regional output;

[0141] a harmonic evaluation and triggering module, configured to perform power flow calculation based on the preliminary output timing sequences of each unit, obtain estimated voltage values of grid nodes, evaluate the harmonic pollution level under the current operating condition, and trigger an amplitude allocation optimization process;

[0142] an amplitude allocation optimization module, configured to, in response to the triggering of the amplitude allocation optimization process, construct an amplitude allocation optimization model for the comprehensive purpose of suppressing harmonic pollution and ensuring node voltage stability, fit a nonlinear relationship between distributed energy output and node voltage, generate a preliminary amplitude allocation constraint set, and solve the amplitude allocation optimization model within the constraint set to obtain a final output amplitude sequence of each energy unit;

[0143] an instruction generation and execution module, configured to integrate the preliminary output timing sequences and the final output amplitude sequence, generate a control instruction sequence sent to each distributed energy unit, and obtain a coordination instruction for suppressing voltage fluctuation and harmonic pollution.

[0144] Obviously, the above embodiments are merely examples for the purpose of clarity and are not intended to limit the embodiments. Based on the above description, those skilled in the art can make other different forms of changes or variations. Here, it is not necessary and impossible to exhaust all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for optimizing power quality in a distribution network with distributed energy access, characterized in that: Includes the following steps: Real-time acquisition of output time-series data of each distributed energy unit in the low-voltage distribution network and voltage data of grid nodes; based on the output time-series data, aggregation to generate the total regional output curve; and calculation of the output fluctuation rate of each unit and the dispersion of the total regional output. With the goal of smoothing the total output curve of the region and reducing its fluctuations, a time-series coordination optimization model is constructed to obtain a set of preliminary output time sequences for each unit that minimizes the fluctuations in the total output of the region. Power flow calculations are performed based on the initial output time series of each unit to obtain the estimated voltage values ​​of the grid nodes, assess the harmonic pollution level under the current operating conditions, and trigger the amplitude allocation optimization process. The process includes the following steps: establishing a grid operation scenario simulator based on the initial output time series, mapping the output of each unit to the distribution network topology model according to the time series; performing multi-timescale rolling power flow calculations, calculating the voltage change trajectory of each grid node over a future period using a progressive time window approach starting from the current moment; constructing a comprehensive voltage quality evaluation matrix, comprehensively analyzing the voltage deviation, fluctuation amplitude, and duration of each node, and identifying the voltage exceedance risk periods and locations; simultaneously conducting harmonic pollution situation assessment, predicting the harmonic distribution under different operating scenarios by analyzing the correlation between the output characteristics of each unit and the harmonic emission characteristics; establishing a voltage harmonic collaborative assessment mechanism, generating an amplitude allocation optimization trigger signal when a spatiotemporal correlation is detected between voltage quality deterioration and harmonic pollution aggravation; and designing an adaptive adjustment strategy for the optimized trigger threshold, dynamically adjusting the trigger conditions according to the grid operating status to ensure the activation of the amplitude allocation optimization process. In response to the triggering of the amplitude allocation optimization process, with the comprehensive objectives of suppressing harmonic pollution and ensuring node voltage stability, an amplitude allocation optimization model is constructed, the nonlinear relationship between distributed energy output and node voltage is fitted, a preliminary amplitude allocation constraint set is generated, and the amplitude allocation optimization model is solved within the constraint set to obtain the final output amplitude sequence of each energy unit. By integrating the initial output timing sequence and the final output amplitude sequence, a control command sequence is generated and sent to each distributed energy unit, thereby obtaining a coordination command to suppress voltage fluctuations and harmonic pollution.

2. The method for optimizing power quality in a distribution network with distributed energy access according to claim 1, characterized in that: Real-time acquisition of output time-series data of each distributed energy unit in the low-voltage distribution network and voltage data of grid nodes includes the following steps: Intelligent monitoring terminals with time synchronization function are deployed at the outlet of each distributed energy unit in the distribution network and at key power grid nodes to form a distributed monitoring network. Configure the acquisition parameters of the intelligent monitoring terminal, set a unified sampling frequency and data format, and start a continuous data acquisition process; Each intelligent monitoring terminal adds timestamps and location identifiers to the raw data streams it collects, forming standardized data packets, which are then uploaded via a hybrid communication network consisting of power line carrier and wireless cellular network. A data cleaning gateway is set up at the network aggregation node to receive the standardized data packets, verify them, and remove outliers and redundant data caused by signal interference to obtain a clean time-series data set.

3. The method for optimizing power quality in a distribution network with distributed energy access according to claim 2, characterized in that: Based on the output time series data, the output fluctuation rate of each unit and the dispersion of the total regional output are calculated, including the following steps: Receive a clean time-series data set, classify it by energy unit, align it along the time axis, and generate independent output change curves for each unit; For each independent output change curve, within a set calculation time window, the absolute average value of the output difference between its adjacent sampling points is calculated, and this value is defined as the real-time output fluctuation rate of the unit. By superimposing the independent output variation curves of all units, the total output curve of the region is obtained. The average deviation of the output of each unit from the average value of the total output of the region within this time window is calculated, and this value is defined as the regional output dispersion.

4. The method for optimizing power quality in a distribution network with distributed energy access according to claim 1, characterized in that: With the goal of smoothing the total power output curve of the region and reducing its fluctuations, a time-series coordinated optimization model is constructed to obtain a set of preliminary power output time sequences for each unit that minimizes the fluctuations in the total power output of the region. This includes the following steps: Establish a quantitative index system for the fluctuation of the regional total power output curve, determine the fluctuation tolerance range based on historical data statistical analysis, and set the dual optimization objectives of maximizing the smoothness of the total power output curve and minimizing the extreme value of fluctuation. Construct a multi-energy unit collaborative constraint library, incorporating the output characteristic parameters, operating status limits, and network transmission capacity constraints of each unit, to form a time-coordinated feasible solution space; A hierarchical optimization architecture is created, firstly by self-optimizing the output smoothness at the unit level, then by coordinating the overall fluctuations at the region level, and finally by verifying the time sequence at the system level. A progressive optimization strategy based on perturbation observation is designed. By repeatedly adjusting the output timing points of each unit in small increments, the smoothness of the total output curve in the observation area is improved, gradually approaching the optimal coordination scheme.

5. The method for optimizing power quality in a distribution network with distributed energy access according to claim 4, characterized in that: Also includes: Develop a time series stability verification mechanism to test the anti-interference capability of the optimized initial output time series and ensure that the coordination effect is maintained within the expected fluctuation range. The system outputs a preliminary power output timing sequence that has been verified for stability, and records the optimal power output time point and power change gradient of each unit, providing a basic timing framework for subsequent amplitude allocation.

6. The method for optimizing power quality in a distribution network with distributed energy access according to claim 1, characterized in that: In response to the triggering of the amplitude allocation optimization process, with the comprehensive objectives of suppressing harmonic pollution and ensuring node voltage stability, an amplitude allocation optimization model is constructed. This model fits the nonlinear relationship between distributed energy output and node voltage, generating a preliminary amplitude allocation constraint set, including the following steps: Establish a multi-objective optimization weight allocation mechanism, dynamically adjust the priority weights of harmonic suppression and voltage stability according to the real-time power grid status, and form a differentiated optimization strategy; Construct a distributed energy output-node voltage response feature library, and mine the impact of output changes of various types of energy units on node voltage through historical operating data; Design a harmonic pollution correlation analysis model and establish a correlation spectrum between harmonic emission characteristics and voltage distortion under different power output conditions; Develop an adaptive constraint generation algorithm to dynamically generate boundary constraints with amplitude allocation based on real-time power grid topology and operating status; Create a multi-dimensional constraint coordination mechanism to coordinate and handle conflicts between various constraints such as equipment operating limits, network transmission capacity, and power quality indicators; Generate a hierarchical constraint set, classify the constraints according to their urgency and scope of influence, and form a preliminary amplitude allocation constraint set that can prioritize the satisfaction of key constraints.

7. The method for optimizing power quality in a distribution network with distributed energy access according to claim 6, characterized in that: Solving the amplitude allocation optimization model within the constraint set to obtain the final output amplitude sequence of each energy unit includes the following steps: Establish a solution process monitoring mechanism to monitor and optimize the solution progress and constraint satisfaction in real time, and dynamically adjust the solution strategy. The design employs a multi-stage incremental solution strategy, firstly rapidly obtaining a feasible solution, and then gradually optimizing the objective function based on the feasible solution. Develop a constraint violation evaluation system to quantitatively evaluate the constraint compliance of temporary solutions generated during the solution process; Construct a comprehensive evaluation model for solution quality to assess the quality of the solution from multiple dimensions, including objective function value, constraint satisfaction, and implementation feasibility. The design incorporates an adaptive termination mechanism to intelligently determine the optimal termination time based on the solution progress and solution quality trends. The system outputs a fully evaluated final output amplitude sequence, while recording the power adjustment range and rate limit of each unit, providing complete amplitude information for the generation of control commands.

8. The method for optimizing power quality in a distribution network with distributed energy access according to claim 1, characterized in that: Integrating the initial output timing sequence and the final output amplitude sequence to generate a control command sequence to be sent to each distributed energy unit, a coordination command to suppress voltage fluctuations and harmonic pollution is obtained, including the following steps: The time coordinates of the initial power output time sequence are fused and matched with the power values ​​of the final power output amplitude sequence to generate a basic instruction set containing two-dimensional time-power attributes; The basic instruction set is smoothed and security verified to ensure the continuity of instruction changes and the safety of equipment operation; Standardized control commands are encapsulated and generated according to a predetermined communication protocol and distributed to each distributed energy unit for execution.

9. A power quality optimization system for a distribution network with distributed energy access, used to implement the method described in any one of claims 1 to 8, characterized in that: include: The data acquisition and status assessment module is used to collect the output time-series data of each distributed energy unit in the low-voltage distribution network and the voltage data of the grid nodes in real time. Based on the output time-series data, it aggregates and generates the total output curve of the region, and calculates the output fluctuation rate of each unit and the dispersion of the total output of the region. The timing coordination and optimization module is used to construct a timing coordination and optimization model with the goal of smoothing the total output curve of the region and reducing its fluctuations, and to obtain a set of preliminary output timing sequences of each unit that minimizes the fluctuations of the total output of the region. The harmonic assessment and triggering module is used to perform power flow calculations based on the initial output time sequence of each unit, obtain the estimated voltage value of the grid node, assess the harmonic pollution level under the current operating conditions, and trigger the amplitude allocation optimization process. The amplitude allocation optimization module is used to respond to the triggering of the amplitude allocation optimization process. With the comprehensive goal of suppressing harmonic pollution and ensuring node voltage stability, it constructs an amplitude allocation optimization model, fits the nonlinear relationship between distributed energy output and node voltage, generates a preliminary amplitude allocation constraint set, solves the amplitude allocation optimization model within the constraint set, and obtains the final output amplitude sequence of each energy unit. The instruction generation and execution module is used to integrate the preliminary output timing sequence and the final output amplitude sequence to generate a control instruction sequence to be sent to each distributed energy unit, thereby obtaining a coordination instruction to suppress voltage fluctuations and harmonic pollution.

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