Micro-grid electric energy quality optimization treatment method and system

Abstract

The invention relates to the technical field of electric energy optimization, and provides a micro-grid electric energy quality optimization treatment method and system. The method comprises the following steps: establishing a micro-grid model which comprises a power generation unit, an adjusting unit and a load group; setting a governance interval, and predicting a power generation power curve and a power utilization power curve in the governance interval; according to the power generation and utilization power curve, simulating to obtain an electric energy quality analysis value of the micro-grid; calling electric energy quality multi-dimensional constraints and judging an analysis value; when the constraint is not met, a control scheme is generated according to the power generation and utilization power curve in combination with an adjusting unit; and according to the control scheme, controlling the power generation and regulation unit in the treatment interval to supply power to the load group. The technical problem that the electric energy quality of the micro-grid is difficult to guarantee under the conditions of complex load requirements and unstable power generation is solved, and the effects of optimizing the electric energy quality of the micro-grid and guaranteeing the power utilization requirements of the industrial park through the multi-mode adjusting unit and accurate matching of the generated power and the load power are achieved.

Description

Technical Field

[0001] The present application relates to the field of electric energy optimization technology, and specifically to a method and system for optimizing and managing electric energy quality in a microgrid. Background Art

[0002] With the adjustment of the global energy structure and the widespread application of renewable energy, microgrids, as a distributed grid technology that can integrate multiple energy forms, have been increasingly used in the power supply of industrial parks, residential areas and other areas. Microgrids have power generation, storage and load management functions, and achieve efficient energy utilization and stable power supply through flexible connection with the main grid. In microgrids, due to the volatility and instability of the output characteristics of renewable energy generation units such as photovoltaic and wind power, the diversity and complexity of loads lead to drastic changes in power demand, which easily cause power quality problems, such as voltage fluctuations, frequency drift, three-phase imbalance and harmonic distortion. These problems not only affect the normal operation of power-consuming equipment, but may also cause equipment damage or system failure. However, traditional power quality management methods are mostly based on the regulation of a single device or power generation unit, which is difficult to cope with complex microgrid structures, especially in an environment where multiple regulation modes coexist and power generation and power consumption fluctuate frequently, the management effect is limited. Therefore, how to ensure power quality in microgrids by optimizing management means has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] This application provides a microgrid power quality optimization management method and system, aiming to solve the technical problem that the power quality of the microgrid is difficult to ensure under complex load demands and unstable power generation conditions.

[0004] In view of the above problems, the present application provides a method and system for optimizing power quality management in a microgrid.

[0005] The first aspect disclosed in the present application provides a method for optimizing and managing power quality of a microgrid, the method comprising: establishing a microgrid model, the microgrid model being established according to a target microgrid, comprising a power generation unit, a regulation unit and an industrial park load group, the regulation unit having a plurality of regulation modes, namely parallel regulation, series regulation and series-parallel mixed regulation; setting a management interval, predicting a power generation curve of the power generation unit and a power consumption curve of the industrial park load group within the management interval; simulating and obtaining a power quality analysis value of the target microgrid within the management interval according to the power generation curve and the power consumption curve; retrieving a multidimensional power quality constraint of the target microgrid, and judging the power quality analysis value based on the multidimensional power quality constraint; when the power quality analysis value does not meet the multidimensional power quality constraint, generating a power optimization control scheme according to the power generation curve and the power consumption curve in combination with the regulation unit; according to the power optimization control scheme, controlling the power generation unit and the regulation unit within the management interval to supply power to the industrial park load group.

[0006] Another aspect disclosed in the present application provides a microgrid power quality optimization management system, the system comprising: a model building module, the model building module is used to build a microgrid model, the microgrid model is built according to the target microgrid, including a power generation unit, a regulation unit and an industrial park load group, the regulation unit has a plurality of regulation modes, namely parallel regulation, series regulation and series-parallel mixed regulation; a curve prediction module, the curve prediction module is used to set a management interval, and predict the power generation curve of the power generation unit and the power consumption curve of the industrial park load group within the management interval; a power quality simulation acquisition module, the power quality simulation acquisition module is used to simulate the power generation curve and the power consumption curve according to the power generation curve and the power consumption curve. The power quality analysis value of the target microgrid within the governance interval is truly obtained; a power quality judgment module, the power quality judgment module is used to retrieve the multidimensional constraints on the power quality of the target microgrid, and judge the power quality analysis value based on the multidimensional constraints on the power quality; a control scheme generation module, the control scheme generation module is used to generate a power optimization control scheme according to the power generation power curve and the power consumption power curve in combination with the adjustment unit when the power quality analysis value does not meet the multidimensional constraints on the power quality; a load group power supply module, the load group power supply module is used to control the power generation unit and the adjustment unit within the governance interval according to the power optimization control scheme, and supply power to the load group in the industrial park.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The above-mentioned microgrid power quality optimization and management method first establishes a microgrid model, which is constructed based on the specific situation of the actual target microgrid, including power generation units, regulation units and load groups in industrial parks. Among them, the regulation unit can select parallel, series or series-parallel mixed regulation mode as needed to flexibly adapt to different power quality requirements. Subsequently, a governance interval is set, and the power generation curve of the power generation unit and the power consumption curve of the industrial park load group in the interval are predicted to understand the power generation and power consumption trends of the microgrid and provide data support for subsequent optimization control. After that, based on the predicted power generation curve and power consumption curve, the power quality analysis value of the microgrid in the governance interval is obtained through simulation, so that the current power quality situation can be comprehensively evaluated. This step of simulation analysis can accurately locate the problem points of power quality and lay the foundation for the subsequent power optimization plan. Then, the multi-dimensional constraints of the power quality of the microgrid are called to check whether the analysis value meets the preset standards. If the analysis value does not meet these constraints, a power optimization control plan is generated based on the power generation and power consumption curves, combined with the various adjustment modes of the regulation unit. This solution can flexibly adjust the operating status of the microgrid at different time points and load conditions to maximize the optimization of power quality. Finally, according to the generated power optimization control plan, the power generation unit and the regulation unit are controlled in real time to supply power to the load group in the industrial park. This dynamic adjustment and optimization process can effectively improve the stability of power quality, reduce voltage fluctuations, frequency deviations and other power quality issues, and ensure that the power demand of the industrial park is met efficiently and stably.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 The present invention is a flow chart of a method for optimizing and managing power quality of a microgrid in one embodiment.

[0012] Figure 2 This is an architecture diagram of a microgrid power quality optimization and management system in one embodiment.

[0013] Explanation of the accompanying drawings: model building module 1, curve prediction module 2, power quality simulation acquisition module 3, power quality judgment module 4, control scheme generation module 5, load group power supply module 6. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method and system for optimizing and managing power quality in a microgrid, thereby solving the technical problem that power quality in a microgrid is difficult to ensure under complex load demands and unstable power generation conditions.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0017] Embodiment 1, as Figure 1 As shown, the present application provides a method for optimizing power quality of a microgrid, the method comprising:

[0018] A microgrid model is established, wherein the microgrid model is established according to a target microgrid and includes a power generation unit, a regulation unit and an industrial park load group. The regulation unit has multiple regulation modes, namely parallel regulation, series regulation and series-parallel mixed regulation.

[0019] In an embodiment of the present application, the system terminal performs a detailed analysis of the actual situation of the target microgrid, including obtaining basic information on the type, scale, and operating status of the target microgrid's power generation equipment, regulation equipment, and load equipment. Power generation equipment may include solar energy, wind energy, gas generator sets, etc. The regulation equipment is responsible for regulating according to power demand and power generation changes. Load equipment includes various types of power loads, such as production line equipment, cooling systems, office appliances, etc. Subsequently, according to the type and structural characteristics of the power generation equipment, a three-dimensional modeling software is used to perform three-dimensional modeling on it to obtain a power generation unit. According to the regulation mode and structural characteristics of the regulation equipment, a three-dimensional modeling software is used to perform three-dimensional modeling on it to obtain a regulation unit. Among them, the regulation mode is divided into a parallel regulation mode, a series regulation mode, a series-parallel hybrid regulation mode, etc. The parallel regulation mode is used to connect multiple power generation units in parallel when the load demand changes to ensure a stable power supply. The series regulation mode is used to deal with voltage or frequency fluctuations, and the overall output is controlled by adjusting different power generation equipment step by step. The series-parallel hybrid regulation mode is used to deal with complex load demands and power quality problems to achieve more flexible control. According to the historical power consumption data and structural characteristics of the load equipment in the industrial park, a three-dimensional modeling software is used to model it and obtain the load group of the industrial park. After completing the independent modeling of the power generation unit, the regulation unit and the industrial park load group, the system terminal integrates each unit and the load into a unified microgrid model according to the actual connection relationship between these units and the load. During the integration process, the output of the power generation unit, the regulation capability of the regulation unit and the changes in the power demand of the load group are considered. By encapsulating the coupling relationship between these units, a microgrid model that can respond dynamically is finally constructed, which can ensure the flexible adjustment of the matching relationship between power generation and power consumption under different regulation modes, and finally achieve the optimization of the power quality of the microgrid.

[0020] A governance interval is set, and a power generation curve of the power generation unit and a power consumption curve of the load group in the industrial park are predicted within the governance interval.

[0021] In one embodiment, the governance interval refers to the time period for optimizing the power quality of the microgrid. When setting the governance interval, the system terminal selects an appropriate time range according to actual needs. The governance interval can be one day, one week, or longer, depending on the periodicity of power generation and power consumption fluctuations. For example, for a photovoltaic power generation unit, a whole day can be selected as the governance interval to capture the impact of light changes over time. After the governance interval is set, the system terminal collects the real-time power generation environment data of the power generation unit in the governance interval to obtain a real-time power generation environment set. The real-time park production data of the industrial park load group is collected to obtain a real-time park production set. Subsequently, the real-time power generation environment set is input into the power generation predictor, and the real-time park production set is input into the power consumption predictor to generate the power generation curve of the power generation unit and the power consumption curve of the industrial park load group. The power generation curve represents the expected power generation at each time point in the governance interval, and the power consumption curve reflects the expected power demand of the park at each time point in the governance interval. By obtaining these two curves, data basis can be provided for subsequent power quality optimization, ensuring that the adjustment unit can play a role when supply and demand are unbalanced and optimize power quality.

[0022] Furthermore, the present application provides a method for predicting the power generation curve of the power generation unit and the power consumption curve of the load group in the industrial park within the treatment interval, including:

[0023] Acquire the historical power generation records of the power generation unit, and synchronously collect the historical environment records corresponding to the historical power generation records, to generate a power generation environment sample set and a power generation sample set; acquire the historical power consumption records of the industrial park load group, and synchronously collect the historical production records corresponding to the historical power consumption records, to generate a park production sample set and a power consumption sample set; establish a power generation predictor based on the power generation environment sample set and the power consumption sample set, and establish a power consumption predictor based on the park production sample set and the power consumption sample set; generate the power generation curve based on the governance interval in combination with the power generation predictor, and generate the power consumption curve in combination with the power consumption predictor.

[0024] Preferably, the system terminal extracts historical power generation data from the power generation equipment corresponding to the power generation unit. These data include the power generation at each time point, for example, the photovoltaic power generation record will include the power output change during the daily light period. Then collect environmental factors related to these power generation data, such as the light intensity of the corresponding time period for photovoltaic power generation, and the wind speed and wind direction for wind power generation. By collecting these data, the environmental conditions can be matched one by one with the power generation to form a power generation environment sample set and a power generation sample set. The power generation environment sample set contains the environmental conditions at each moment in history, and the power generation sample set contains the power generation at each corresponding moment. Similarly, by monitoring the load equipment corresponding to the load group of the industrial park, their historical power consumption data are extracted to reflect the power consumption in different time periods. These data are usually recorded by hours or minutes. Then collect production data related to the power consumption record. These data include production shifts, equipment start and stop times, production task types, etc. These production records can help explain the reasons for the fluctuation of power consumption, such as high power demand caused by production peaks during a specific period of time. The power consumption record is associated with the production record to generate a park production sample set and a power consumption sample set. The production sample set of the park records the production situation in each period, while the power consumption sample set reflects the power consumption of the load group in each period. Subsequently, the initial power generation predictor is constructed by using multilayer perceptron, time series model, regression model and other methods, and the power generation environment sample set and power generation sample set are input into the initial power generation predictor. Through multiple iterative training, the initial power generation predictor is gradually made close to the expected effect. Taking the multilayer perceptron as an example, the system terminal uses the multilayer perceptron to construct the initial power generation predictor structure, including the input layer, hidden layer, output layer, and the number of neurons in each layer. Then the power generation environment sample set is input into the initial power generation predictor for forward propagation, and the power generation prediction value under the current environmental conditions is calculated by layer-by-layer transmission. Subsequently, the mean square error MSE is used to calculate the loss value between the power generation prediction value and the corresponding power generation sample, and then the gradient of the loss to the weight of each layer is calculated layer by layer through the back propagation algorithm. After that, the parameters of the initial power generation predictor are optimized by using the Adam optimizer, gradient descent method and other methods, and the weights are adjusted to minimize the value of the loss function. Repeat the above process until the loss value calculated by the loss function is less than or equal to the loss threshold. When the training is completed, the system terminal uses the data that did not participate in the training to evaluate the initial power generation predictor, calculate the prediction error of the initial power generation predictor, and judge its generalization ability. If the prediction error is less than or equal to the loss threshold, the current initial power generation predictor is output as the final power generation predictor. Otherwise, adjust the hyperparameters such as the learning rate and the number of training batches to further improve the prediction ability of the power generation predictor.For the power generation predictor, the system terminal uses the same training method as above to train the initial power generation predictor using the park production sample set and the power consumption sample set to obtain the final power generation predictor. Among them, the initial power generation predictor is also constructed using multilayer perceptron, time series model, regression model and other methods. After constructing the power generation predictor and the power consumption predictor, the system terminal generates the power generation curve in the governance interval through the power generation predictor and the real-time power generation environment data in the governance interval. This curve reflects the power generation change of the power generation unit in each period in the governance interval, which is obtained by fitting the predicted power generation at each moment. Through the power consumption predictor, combined with the real-time park production data in the governance interval, the power consumption curve in the governance interval is generated. This curve represents the change in power demand of the industrial park load group in the governance interval, which is obtained by fitting the predicted power consumption at each moment. Through this process, the system terminal obtains the power generation curve and the power consumption curve, which provides basic data for power optimization and ensures the balance of power generation and power consumption in the governance interval.

[0025] According to the power generation curve and the power consumption curve, the power quality analysis value of the target microgrid within the governance interval is obtained by simulation.

[0026] In one embodiment, after obtaining the power generation curve and the power consumption curve, the system terminal sets the simulation boundary conditions of the microgrid model according to the power generation curve and the power consumption curve. Subsequently, the microgrid model is used to simulate the operation of the target microgrid in the governance interval, and the power quality data at each time point in the governance interval is obtained. Based on these power quality data, various indicators of power quality, such as voltage, frequency, harmonic distortion, etc., are analyzed, and power quality analysis values ​​are generated. The power quality analysis value reflects the power quality performance of the target microgrid in the governance interval, providing data support for subsequent power optimization.

[0027] Furthermore, the present application provides a method for simulating and obtaining a power quality analysis value of the target microgrid within a governance interval according to the power generation curve and the power consumption curve, including:

[0028] The power generation curve and the power consumption curve are input into the microgrid model as simulation boundary conditions; power quality assessment indicators are set, and the power quality assessment indicators include voltage qualification rate, frequency qualification rate, three-phase current imbalance, voltage total harmonic distortion rate and current total harmonic distortion rate; based on the microgrid model and simulation boundary conditions, the operating state of the target microgrid in the governance interval is simulated and analyzed to obtain power quality data at each time point in the governance interval; according to the power quality assessment indicators, the power quality data is statistically analyzed to obtain the power quality analysis value in the governance interval.

[0029] Preferably, the system terminal inputs the power generation curve and the power consumption curve into the microgrid model as the boundary conditions of the simulation to determine the matching relationship between power generation and power consumption. In order to evaluate the operating status of the target microgrid in different time periods, the system terminal sets multiple power quality evaluation indicators, including but not limited to voltage qualification rate, frequency qualification rate, three-phase current imbalance, voltage total harmonic distortion rate and current total harmonic distortion rate. Among them, the voltage qualification rate measures whether the voltage is maintained within the specified range to ensure the normal operation of the equipment. The higher the qualification rate, the more stable the voltage. The frequency qualification rate is used to evaluate whether the frequency of the microgrid is within the specified standard range to ensure the frequency stability of the system. The three-phase current imbalance evaluates the balance of the three-phase current and reduces the damage of the unbalanced current to the system and equipment. The voltage total harmonic distortion rate is used to measure the degree of distortion of the harmonic components in the voltage waveform. Maintaining a low harmonic distortion rate helps to improve the power quality. The current total harmonic distortion rate is used to evaluate the harmonic distortion in the current waveform to ensure that the equipment will not be damaged due to excessive harmonics. Subsequently, in the microgrid model, the system terminal gradually simulates the operating status of the microgrid in the governance interval based on the power generation curve and the power consumption curve according to the set time step (such as per second or per minute). In each time step, the power generation unit and the regulation unit of the target microgrid will respond according to the current load demand, simulate the distribution process of electric energy in the microgrid, and then dynamically adjust the power flow in the microgrid according to the power generation and power consumption curves, and generate the power quality parameters in the corresponding time period. When the simulation detects that the power consumption exceeds the power generation, the regulation unit will be automatically called to respond. For example, parallel regulation can combine multiple power generation units to supply power, and series regulation can adjust the voltage output of each unit to make the power distribution more balanced. In each time step, the system terminal will record the generated power quality data such as voltage, frequency, and harmonic distortion rate. Then, by statistically analyzing the data recorded during the entire simulation process, the power quality data at each time point in the governance interval is obtained. Through these data, it can be understood whether the power quality in the microgrid at each time point meets the requirements. Afterwards, the system terminal counts the power quality data according to the power quality assessment index to obtain the power quality analysis value within the governance interval. Specifically, the voltage qualification rate is obtained by calculating the proportion of time points in the governance interval when the voltage is qualified to the total time. The frequency qualification rate is obtained by calculating the proportion of time points in the governance interval when the frequency remains within the qualified range. By calculating the average value of the three-phase current, and then subtracting each phase current from the average value, the maximum deviation value is identified, and then the three-phase current imbalance is obtained by dividing the maximum deviation value by the average value. The harmonic distortion rate is obtained by calculating the ratio of the square root of the sum of the squares of each harmonic component to the effective value of the fundamental wave. Then, based on all statistical analysis results, the system terminal summarizes the overall power quality analysis value within the governance interval. This analysis value reflects the power quality performance of the target microgrid throughout the simulation cycle.

[0030] The multidimensional constraints on the power quality of the target microgrid are retrieved, and the power quality analysis value is judged based on the multidimensional constraints on the power quality; when the power quality analysis value does not meet the multidimensional constraints on the power quality, a power optimization control scheme is generated according to the power generation curve and the power consumption curve in combination with the adjustment unit.

[0031] In one embodiment, the system terminal retrieves the pre-set multi-dimensional constraints on the power quality of the target microgrid. These constraints are standards or restrictions set for multiple indicators of power quality, including but not limited to voltage qualification rate, frequency qualification rate, three-phase current imbalance, harmonic distortion rate, etc. Each indicator has a corresponding upper or lower limit value, and these standards are used to ensure that the power quality of the microgrid meets the requirements under different working conditions. Subsequently, the simulated power quality analysis value is compared with the retrieved multi-dimensional constraints one by one to check whether each analysis value is within the constraint range. For example, check whether the voltage is within the specified range, whether the frequency is stable, whether the three-phase current is balanced, etc. If all indicators meet the constraints, the power quality is considered to be qualified; otherwise, if some or all indicators fail to meet the standards, further measures need to be taken. When it is found that there are power quality analysis values ​​that do not meet the set multi-dimensional constraints, the system terminal generates an optimization control scheme based on the power generation curve and the power consumption curve, combined with the regulation unit in the microgrid and the constraint violation time window. The core of this control scheme is to balance power generation and power demand by adjusting the operating parameters of the power generation unit and the regulation unit. For example, if the electricity demand is higher than the power generation capacity during a certain period of time, the power quality can be optimized by increasing the power generation output or reducing the load demand through parallel or series regulation mode.

[0032] Furthermore, the present application provides a method for generating an energy optimization control scheme based on the power generation curve and the power consumption curve in combination with the adjustment unit, including:

[0033] The power quality analysis value is compared with the power quality multidimensional constraint to determine multiple constraint violation time windows; according to the power quality multidimensional constraint, the power quality deviation indicators within the multiple constraint violation time windows are judged in turn, and the power quality deviation amount corresponding to the power quality deviation indicator is obtained; based on the power quality deviation indicator and the power quality deviation amount, the adjustment mode of the adjustment unit within each constraint violation time window is selected to obtain multiple adjustment modes; for each constraint violation time window, multiple window control schemes are generated in combination with the corresponding power quality deviation indicator, the power quality deviation amount and the adjustment mode, and the power optimization control scheme is summarized and generated.

[0034] Preferably, the system terminal compares the power quality analysis value obtained by simulation with the retrieved power quality multidimensional constraints one by one, and checks whether the power quality at each time point or time period meets the set standard. For any time point or time period that does not meet the constraint conditions, the system terminal will mark it as a constraint violation time window, that is, in these time windows, the power quality does not meet the standard and needs to be adjusted. For each constraint violation time window, the system terminal will evaluate the various deviation indicators of power quality in turn, such as voltage deviation, frequency deviation, harmonic distortion rate deviation, etc. These deviation indicators are based on the difference between the current power quality and the preset standard. Subsequently, the system terminal calculates the deviation amount of each deviation indicator, that is, the specific difference between the actual measured power quality indicator and the set standard. For example, if the voltage in a certain time period is lower than the qualified standard, the voltage deviation amount of the time window will be recorded. After determining the power quality deviation amount in the constraint violation time window, the system terminal selects the appropriate adjustment mode of the adjustment unit according to these deviation amounts. Common adjustment modes include parallel adjustment, series adjustment, and series-parallel mixed adjustment. This selection process is based on the size and type of the deviation and the current operating state of the system. For example, if the voltage deviation is too large, the system terminal will select the parallel mode to increase the power output. After that, the system terminal combines the power quality deviation index, the power quality deviation amount and the regulation mode to generate a window control scheme. Then, according to this window control scheme and the power generation curve and the power consumption curve, the simulation is performed again to obtain the power quality analysis value after this optimization. Then, using the same method as above, it is determined whether the optimized power quality analysis value still has a constraint violation time window. If so, the above optimization process is performed again to determine the regulation mode, and to determine whether the newly determined regulation mode is the same as the previous regulation mode. If the same, the subsequent operation will continue. If different, the improvement benefit analysis will be performed to determine the regulation mode to be used. Repeat the above process until there is no or less abnormality in the power quality analysis value. At this time, the system terminal will summarize all the generated window control schemes to obtain the power optimization control scheme. This optimization scheme will be gradually implemented in the governance interval. By adjusting the matching relationship between power generation and power consumption, and dynamically adjusting the working mode of the regulation unit, it is ensured that the power quality of the target microgrid reaches or is as close to the preset standard as possible at all time points.

[0035] Furthermore, the present application provides a power optimization control scheme, including:

[0036] A first constraint violation time window is obtained from multiple constraint violation time windows, and the corresponding first power quality deviation index and first power quality deviation amount are extracted; a first adjustment mode for the first constraint violation time window is determined according to the first power quality deviation index and the first power quality deviation amount; a first window control scheme is generated based on the first power quality deviation index, the first power quality deviation amount and the first adjustment mode; according to the first window control scheme, the power generation curve and the power consumption curve, in combination with the adjustment unit, a first optimized power quality analysis value of the target microgrid within the governance interval is simulated and obtained; multiple updated constraint violation time windows are determined according to the first optimized power quality analysis value, a first updated constraint violation time window is obtained, and a second window control scheme is generated; iterative execution is performed, and the multiple window control schemes generated are summarized to obtain a power optimization control scheme.

[0037] Optionally, in multiple constraint violation time windows, the system terminal extracts the first constraint violation time window as the first constraint violation time window. This is the time period in which the power quality deviation first occurs. In this time window, the power quality index does not meet the preset multidimensional constraint standard and needs to be adjusted. For this first constraint violation time window, the system terminal extracts deviation indicators related to power quality, such as voltage, frequency or harmonic distortion rate, and then uses the same method to calculate the deviation amount of these deviation indicators to obtain the first power quality deviation indicator and the first power quality deviation amount. Subsequently, according to the power quality deviation indicator and the deviation amount of the first constraint violation time window, the system terminal uses the same method as above to select a suitable first adjustment mode, and then sorts out the first power quality deviation indicator, the first power quality deviation amount and the selected first adjustment mode to generate a first window control scheme. The scheme defines how to adjust the power generation unit and the adjustment unit of the microgrid in this time window to improve the power quality. Afterwards, according to the first window control scheme, combined with the power generation power curve and the power consumption power curve, the same simulation is performed again, and the first optimized power quality analysis value in the governance interval is obtained. The result of this step will show whether the power quality of the target microgrid in the time window has improved after the control scheme is implemented. After the simulation, the system terminal will determine whether there are still time windows whose power quality does not meet the preset standard based on the first optimized power quality analysis value. If there are still other time periods with power quality problems, they will be identified and marked as update constraint violation time windows. These windows represent time periods where the power quality does not meet the standard even after adjustment. These new windows may be different from the initial windows because the adjusted power quality has changed. Some windows may no longer violate the constraints, while new time periods may have deviations. After obtaining multiple update constraint violation time windows, the system terminal selects the first update constraint violation time window as the first update constraint violation time window, and extracts the first update power quality deviation index and the first update power quality deviation amount of the window. According to the new deviation, the system terminal generates a second window control scheme for the time period, similar to the previous steps, to readjust the power generation and regulation units to further improve the power quality. This process will be iteratively executed, and a new window control scheme will be generated each time, and the power quality improvement will be simulated and analyzed. As more window control schemes are generated and executed, the system terminal gradually optimizes the power quality of the target microgrid. Finally, the system terminal will summarize all window control schemes to obtain a complete power optimization control scheme, covering the power quality optimization strategy for all time periods in the governance interval. Through the above iterative simulation and control scheme adjustment, it can be ensured that the power quality of the target microgrid in the entire governance interval meets the expected standards.

[0038] Furthermore, the present application provides a second window control scheme, including:

[0039] A second adjustment mode is generated for the first update constraint violation time window; when the first adjustment mode is different from the second adjustment mode, an adjustment mode switching loss is obtained; through the second adjustment mode, an electric energy quality optimization gain after adjusting the first update constraint violation time window is obtained; when the electric energy quality optimization gain is greater than the adjustment mode switching loss, the second adjustment mode is adopted to generate a second window control scheme; when the electric energy quality optimization gain is less than or equal to the adjustment mode switching loss, the first adjustment mode is maintained to generate a second window control scheme.

[0040] Optionally, after simulation, if there is still a constraint violation time window, the system terminal will identify the first updated constraint violation time window as the first updated constraint violation time window. In this first updated constraint violation time window, a second regulation mode is selected according to the first updated power quality deviation value, which may be different from the previous first regulation mode, depending on the deviation type and deviation amount of the current power quality. The selection of the regulation mode will be optimized according to the current situation, and parallel regulation, series regulation or mixed regulation mode may be selected to optimize the power quality. If the second regulation mode is different from the previous first regulation mode, the system terminal will obtain the regulation mode switching loss. The regulation mode switching loss refers to the increase in operating cost or decrease in efficiency that may be caused when switching between different modes. For example, switching from parallel mode to series mode may involve conversion losses of voltage, frequency, harmonics, etc., which will affect the operation of the system. Therefore, the system terminal uses the microgrid model to simulate and determine what the optimization gain of the power quality is when the second regulation mode is adopted. The optimization gain refers to the degree to which various indicators of power quality are improved after the second regulation mode is applied. Through simulation calculation, it is possible to determine how much actual power quality improvement can be brought about by adopting the second regulation mode, and obtain the power quality optimization gain. Subsequently, the system terminal compares the power quality optimization gain with the regulation mode switching loss. If the power quality optimization gain is greater than the regulation mode switching loss, it means that the second regulation mode will be beneficial to the improvement of power quality. Therefore, the second regulation mode is selected and the second window control scheme is generated for control within the time window. If the power quality optimization gain is less than or equal to the regulation mode switching loss, it means that the cost of switching the regulation mode is too high. At this time, the system terminal will maintain the first regulation mode and generate the second window control scheme to take into account the cost of mode switching and the benefits of power quality optimization, ensuring that the power quality of the target microgrid within the governance interval can be continuously improved under the optimal control scheme.

[0041] According to the electric energy optimization control scheme, the power generation unit and the regulating unit are controlled within the management interval to supply power to the load group in the industrial park.

[0042] In one embodiment, after obtaining the power optimization control scheme, the system terminal coordinates and controls the power generation unit and the regulation unit in the target microgrid within the governance interval according to the power optimization control scheme to ensure that the load group in the industrial park can obtain stable and high-quality power supply. The power generation unit and the regulation unit will be dynamically adjusted according to the power optimization control scheme to ensure that the power generation can adapt to the changes in load demand to balance the power output and prevent voltage fluctuations or frequency instability. In this process, the regulation unit will continuously monitor the real-time power quality data, automatically adjust the voltage and frequency, and ensure the balance of the three-phase current and minimize the harmonic distortion rate. At the same time, the critical loads and non-critical loads in the load group will also be prioritized according to the instructions of the control scheme to ensure that important production equipment is given priority power supply to avoid affecting production.

[0043] Furthermore, the present application provides optimized power supply to the first load group, including:

[0044] The industrial park load group is divided into a first load group and a second load group, wherein the first load group is a critical load and the second load group is a non-critical load; when the power quality analysis value still does not meet the multi-dimensional power quality constraint after simulating the power optimization control scheme, the power supply to the second load group is interrupted, and the power optimization control scheme is re-formulated for the first load group to generate an updated power optimization control scheme; according to the updated power optimization control scheme, the power generation unit and the regulation unit are regulated within the governance interval to optimize the power supply to the first load group.

[0045] Preferably, the system terminal divides the load group of the industrial park into two parts: the first load group and the second load group. Among them, the first load group is a critical load, which usually includes important equipment, core systems or power requirements that have a significant impact on production on the production line, and they need to give priority to power supply. The second load group is a non-critical load, which may be auxiliary equipment, non-core facilities or other power-consuming equipment that will not have a significant impact on production if the power supply is interrupted in a short time. After the system terminal runs the simulation according to the power optimization control scheme, it is found that the power quality analysis value still fails to meet the multidimensional constraints, indicating that the existing power distribution and regulation measures cannot completely solve the power quality problem. At this time, the system terminal will take measures to interrupt the power supply to the second load group, and reduce the load pressure by removing non-critical loads from the microgrid to give priority to the power supply to the first load group. Subsequently, according to the same method as above, the power optimization is re-performed for the needs of the first load group, and a new control scheme is formulated, that is, the updated power optimization control scheme. This updated power optimization control scheme will focus on how to ensure that the first load group can obtain a stable and high-quality power supply by regulating the power generation unit and the regulation unit after removing the second load group. In actual operation, the power generation unit will adjust the power generation output according to the updated power optimization control scheme to match the power demand of the first load group. At the same time, the regulation unit will dynamically optimize the power quality parameters such as voltage and frequency to ensure that the balance of three-phase current and the harmonic distortion rate are within a controllable range, thereby improving the power quality. By interrupting the power supply of non-critical loads and optimizing the power supply of critical loads, the system terminal can effectively ensure that key production activities in the industrial park are not affected by power quality problems and maintain the normal progress of core production.

[0046] In summary, the embodiments of the present application have at least the following technical effects:

[0047] The embodiment of the present application first establishes a microgrid model, which includes a power generation unit, a regulation unit and an industrial park load group, wherein the regulation unit has parallel regulation, series regulation and series-parallel mixed regulation modes. Subsequently, according to the governance interval, the power generation curve of the power generation unit and the power consumption curve of the load group are predicted, and these curves are used to simulate the power quality analysis value of the microgrid in the governance interval, and compared with the multi-dimensional constraints of the power quality. If the analysis value does not meet the constraints, the operation of the power generation and regulation units is adjusted by generating an optimization control scheme to ensure the power supply to the industrial park load group. During the simulation operation, if the power quality is still not up to standard, the load group will be divided into critical loads and non-critical loads, and the power supply to non-critical loads will be interrupted, and the optimization scheme will be re-formulated to provide priority power supply for critical loads. These technical effects jointly solve the technical problem that the power quality of the microgrid is difficult to guarantee under complex load demands and unstable power generation conditions, and realize the effect of optimizing the power quality of the microgrid and ensuring the power demand of the industrial park through the precise matching of the multi-mode regulation unit and the power generation power and the load power.

[0048] Embodiment 2, based on the same inventive concept as a microgrid power quality optimization management method in the above embodiment, such as Figure 2 As shown, the present application provides a microgrid power quality optimization and management system, the system comprising: a model building module 1: the model building module 1 is used to establish a microgrid model, the microgrid model is established according to the target microgrid, including a power generation unit, a regulation unit and an industrial park load group, the regulation unit has a plurality of regulation modes, namely parallel regulation, series regulation and series-parallel mixed regulation; a curve prediction module 2: the curve prediction module 2 is used to set a management interval, and predict the power generation curve of the power generation unit and the power consumption curve of the industrial park load group within the management interval; a power quality simulation acquisition module 3: the power quality simulation acquisition module 3 is used to simulate and acquire the power generation curve and the power consumption curve according to the power generation curve and the power consumption curve. The power quality analysis value of the target microgrid within the governance interval; power quality judgment module 4: the power quality judgment module 4 is used to retrieve the multidimensional constraints on the power quality of the target microgrid, and judge the power quality analysis value based on the multidimensional constraints on the power quality; control scheme generation module 5: the control scheme generation module 5 is used to generate a power optimization control scheme according to the power generation power curve and the power consumption power curve in combination with the adjustment unit when the power quality analysis value does not meet the multidimensional constraints on the power quality; load group power supply module 6: the load group power supply module 6 is used to control the power generation unit and the adjustment unit within the governance interval according to the power optimization control scheme to supply power to the load group in the industrial park.

[0049] Furthermore, the curve prediction module 2 is also used to perform the following method:

[0050] Acquire the historical power generation records of the power generation unit, and synchronously collect the historical environment records corresponding to the historical power generation records, to generate a power generation environment sample set and a power generation sample set; acquire the historical power consumption records of the industrial park load group, and synchronously collect the historical production records corresponding to the historical power consumption records, to generate a park production sample set and a power consumption sample set; establish a power generation predictor based on the power generation environment sample set and the power consumption sample set, and establish a power consumption predictor based on the park production sample set and the power consumption sample set; generate the power generation curve based on the governance interval in combination with the power generation predictor, and generate the power consumption curve in combination with the power consumption predictor.

[0051] Furthermore, the power quality simulation acquisition module 3 is also used to execute the following method:

[0052] The power generation curve and the power consumption curve are input into the microgrid model as simulation boundary conditions; power quality assessment indicators are set, and the power quality assessment indicators include voltage qualification rate, frequency qualification rate, three-phase current imbalance, voltage total harmonic distortion rate and current total harmonic distortion rate; based on the microgrid model and simulation boundary conditions, the operating state of the target microgrid in the governance interval is simulated and analyzed to obtain power quality data at each time point in the governance interval; according to the power quality assessment indicators, the power quality data is statistically analyzed to obtain the power quality analysis value in the governance interval.

[0053] Furthermore, the control scheme generating module 5 is also used to execute the following method:

[0054] The power quality analysis value is compared with the power quality multidimensional constraint to determine multiple constraint violation time windows; according to the power quality multidimensional constraint, the power quality deviation indicators within the multiple constraint violation time windows are judged in turn, and the power quality deviation amount corresponding to the power quality deviation indicator is obtained; based on the power quality deviation indicator and the power quality deviation amount, the adjustment mode of the adjustment unit within each constraint violation time window is selected to obtain multiple adjustment modes; for each constraint violation time window, multiple window control schemes are generated in combination with the corresponding power quality deviation indicator, the power quality deviation amount and the adjustment mode, and the power optimization control scheme is summarized and generated.

[0055] Furthermore, the control scheme generating module 5 is also used to execute the following method:

[0056] A first constraint violation time window is obtained from multiple constraint violation time windows, and the corresponding first power quality deviation index and first power quality deviation amount are extracted; a first adjustment mode for the first constraint violation time window is determined according to the first power quality deviation index and the first power quality deviation amount; a first window control scheme is generated based on the first power quality deviation index, the first power quality deviation amount and the first adjustment mode; according to the first window control scheme, the power generation curve and the power consumption curve, in combination with the adjustment unit, a first optimized power quality analysis value of the target microgrid within the governance interval is simulated and obtained; multiple updated constraint violation time windows are determined according to the first optimized power quality analysis value, a first updated constraint violation time window is obtained, and a second window control scheme is generated; iterative execution is performed, and the multiple window control schemes generated are summarized to obtain a power optimization control scheme.

[0057] Furthermore, the control scheme generating module 5 is also used to execute the following method:

[0058] A second adjustment mode is generated for the first update constraint violation time window; when the first adjustment mode is different from the second adjustment mode, an adjustment mode switching loss is obtained; through the second adjustment mode, an electric energy quality optimization gain after adjusting the first update constraint violation time window is obtained; when the electric energy quality optimization gain is greater than the adjustment mode switching loss, the second adjustment mode is adopted to generate a second window control scheme; when the electric energy quality optimization gain is less than or equal to the adjustment mode switching loss, the first adjustment mode is maintained to generate a second window control scheme.

[0059] Furthermore, the load group power supply module 6 is also used to perform the following method:

[0060] The industrial park load group is divided into a first load group and a second load group, wherein the first load group is a critical load and the second load group is a non-critical load; when the power quality analysis value still does not meet the multi-dimensional power quality constraint after simulating the power optimization control scheme, the power supply to the second load group is interrupted, and the power optimization control scheme is re-formulated for the first load group to generate an updated power optimization control scheme; according to the updated power optimization control scheme, the power generation unit and the regulation unit are regulated within the governance interval to optimize the power supply to the first load group.

[0061] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0063] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing power quality of a microgrid, characterized in that: include: Establishing a microgrid model, wherein the microgrid model is established according to the target microgrid, and includes a power generation unit, a regulation unit, and an industrial park load group, wherein the regulation unit has multiple regulation modes, namely, parallel regulation, series regulation, and series-parallel hybrid regulation; Setting a governance interval, and predicting a power generation curve of the power generation unit and a power consumption curve of the load group in the industrial park within the governance interval; According to the power generation curve and the power consumption curve, a power quality analysis value of the target microgrid within the governance interval is obtained by simulation; Retrieving the multidimensional constraints of power quality of the target microgrid, and judging the power quality analysis value based on the multidimensional constraints of power quality; When the power quality analysis value does not satisfy the power quality multi-dimensional constraint, generating a power optimization control scheme according to the power generation curve and the power consumption curve in combination with the adjustment unit; According to the electric energy optimization control scheme, the power generation unit and the regulating unit are controlled within the management interval to supply power to the load group in the industrial park.

2. A microgrid power quality optimization and management method according to claim 1, characterized in that: Predicting the power generation curve of the power generation unit and the power consumption curve of the load group in the industrial park within the control interval includes: Acquire the historical power generation records of the power generation unit, and simultaneously collect the historical environment records corresponding to the historical power generation records to generate a power generation environment sample set and a power generation sample set; Obtain the historical electricity consumption records of the load group in the industrial park, and simultaneously collect the historical production records corresponding to the historical electricity consumption records to generate a park production sample set and an electricity consumption sample set; Based on the power generation environment sample set and the power generation sample set, a power generation power predictor is established, and based on the park production sample set and the power consumption sample set, an electric power consumption predictor is established; According to the control interval, the power generation curve is generated in combination with the power generation predictor, and the power consumption curve is generated in combination with the power consumption predictor.

3. A microgrid power quality optimization and management method according to claim 1, characterized in that: According to the power generation curve and the power consumption curve, a power quality analysis value of the target microgrid within the governance interval is obtained by simulation, including: Inputting the power generation curve and the power consumption curve into the microgrid model as simulation boundary conditions; Setting power quality assessment indicators, wherein the power quality assessment indicators include voltage qualification rate, frequency qualification rate, three-phase current imbalance, voltage total harmonic distortion rate and current total harmonic distortion rate; Based on the microgrid model and simulation boundary conditions, a simulation analysis is performed on the operating state of the target microgrid within the governance interval to obtain power quality data at each time point within the governance interval; According to the power quality assessment index, the power quality data is statistically analyzed to obtain the power quality analysis value within the control interval.

4. A microgrid power quality optimization and management method according to claim 1, characterized in that: According to the power generation curve and the power consumption curve, in combination with the adjustment unit, an electric energy optimization control scheme is generated, including: Comparing the power quality analysis value with the power quality multidimensional constraint to determine a plurality of constraint violation time windows; According to the multi-dimensional constraints on power quality, the power quality deviation indicators within the time window of multiple constraint violations are judged in turn, and the power quality deviation amount corresponding to the power quality deviation indicator is obtained; Selecting a regulation mode of a regulation unit within each constraint violation time window based on the power quality deviation index and the power quality deviation amount to obtain a plurality of regulation modes; For each constraint violation time window, multiple window control schemes are generated in combination with the corresponding power quality deviation index, power quality deviation amount and regulation mode, and then the power optimization control scheme is summarized.

5. A microgrid power quality optimization and management method according to claim 4, characterized in that: The method further comprises: Obtaining a first constraint violation time window from a plurality of constraint violation time windows, and extracting a corresponding first power quality deviation indicator and a first power quality deviation amount; Determining a first adjustment mode for a first constraint violation time window according to the first power quality deviation indicator and the first power quality deviation amount; generating a first window control scheme based on the first power quality deviation indicator, the first power quality deviation amount and the first adjustment mode; According to the first window control scheme, the power generation curve and the power consumption curve, in combination with the adjustment unit, a first optimized power quality analysis value of the target microgrid within the governance interval is obtained by simulation; Determine a plurality of update constraint violation time windows according to the first optimized power quality analysis value, obtain a first update constraint violation time window, and generate a second window control scheme; Iterative execution aggregates the generated multiple window control schemes to obtain the power optimization control scheme.

6. A microgrid power quality optimization and management method according to claim 5, characterized in that: The method further comprises: generating a second adjustment mode for the first update constraint violation time window; When the first regulation mode is different from the second regulation mode, obtaining a regulation mode switching loss; Obtaining, by means of the second adjustment mode, a power quality optimization gain after adjusting the first update constraint violation time window; When the power quality optimization gain is greater than the regulation mode switching loss, adopting the second regulation mode to generate a second window control scheme; When the power quality optimization gain is less than or equal to the regulation mode switching loss, the first regulation mode is maintained and a second window control scheme is generated.

7. A microgrid power quality optimization and management method according to claim 1, characterized in that: The method further comprises: Dividing the load group of the industrial park into a first load group and a second load group, wherein the first load group is a critical load and the second load group is a non-critical load; When the power quality analysis value still does not satisfy the power quality multi-dimensional constraint after the power optimization control scheme is simulated and run, the power supply to the second load group is interrupted, and the power optimization control scheme is re-formulated for the first load group to generate an updated power optimization control scheme; According to the updated power optimization control scheme, the power generation unit and the regulating unit are regulated within the management interval to optimize power supply to the first load group.

8. A microgrid power quality optimization and management system, characterized in that: The steps for implementing a microgrid power quality optimization management method as described in any one of claims 1 to 7 include: Model building module: building a microgrid model, the microgrid model is built according to the target microgrid, including a power generation unit, a regulation unit and an industrial park load group, the regulation unit has multiple regulation modes, namely parallel regulation, series regulation and series-parallel hybrid regulation; Curve prediction module: setting a management interval, predicting the power generation curve of the power generation unit and the power consumption curve of the industrial park load group within the management interval; Power quality simulation acquisition module: according to the power generation curve and the power consumption curve, simulate and acquire the power quality analysis value of the target microgrid within the governance interval; Power quality judgment module: retrieves the power quality multi-dimensional constraints of the target microgrid, and judges the power quality analysis value based on the power quality multi-dimensional constraints; A control scheme generating module: when the power quality analysis value does not satisfy the power quality multi-dimensional constraint, generating a power optimization control scheme according to the power generation curve and the power consumption curve in combination with the regulating unit; Load group power supply module: according to the power optimization control scheme, the power generation unit and the regulating unit are controlled within the management interval to supply power to the load group in the industrial park.

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