Microgrid bus voltage management method based on multi-source collaborative optimization

Abstract

The invention discloses a micro-grid bus voltage management method based on multi-source collaborative optimization, and relates to the technical field of smart grids, and the method comprises the steps: obtaining demand information and power generation characteristic information in a micro-grid, carrying out the voltage fluctuation prediction of a bus, and obtaining the voltage fluctuation information of the bus; a reactive compensation adjustment space, a demand adjustment space and an energy storage voltage adjustment space are obtained, trial optimization of voltage stability control management is carried out, and reactive fluctuation information, demand performance reduction information and energy storage aging information are obtained; and adjusting the reactive compensation adjustment space, the demand adjustment space and the energy storage voltage adjustment space, optimizing multi-source cooperative bus voltage management, obtaining an optimal bus voltage management scheme, and managing and controlling the bus voltage of the micro-grid. The technical problems that in the prior art, an effective coordination mechanism is lacked during voltage coordination regulation and control, and voltage fluctuation is difficult to accurately control are solved, and the technical effect of accurately controlling the bus voltage of the micro-grid is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart power grids, and particularly to a method for managing the bus voltage of a microgrid based on multi-source collaborative optimization. Background Art

[0002] With the rapid development of distributed energy and microgrid technologies, modern power systems are gradually shifting towards distributed generation and renewable energy. Renewable energy sources such as photovoltaic power generation and wind power generation are widely used in microgrids. However, due to the volatility and uncertainty of these energy sources, it poses a huge challenge to the voltage stability of the power grid. When traditional microgrid bus voltage management methods perform coordinated regulation of reactive power compensation, demand-side management, and energy storage systems, there is a lack of an effective coordination mechanism, resulting in the problem that voltage fluctuations are difficult to accurately control. Summary of the Invention

[0003] The present application provides a method for managing the bus voltage of a microgrid based on multi-source collaborative optimization, which is used to solve the technical problem that the existing technology lacks an effective coordination mechanism in the coordinated regulation of voltage and there is a problem that voltage fluctuations are difficult to accurately control.

[0004] In view of the above problems, the present application provides a method for managing the bus voltage of a microgrid based on multi-source collaborative optimization.

[0005] The present application provides a method for managing the bus voltage of a microgrid based on multi-source collaborative optimization, and the method includes:

[0006] Obtain the demand information of the demand side and the power generation characteristic information of the power generation side in the microgrid, perform voltage fluctuation prediction of the bus in the microgrid, and obtain bus voltage fluctuation information; obtain the reactive power compensation adjustment space of the power generation side, the demand adjustment space of the demand side, and the energy storage voltage regulation space of the energy storage side in the microgrid, and respectively perform trial optimization of voltage stability control management on the bus voltage fluctuation information to obtain reactive power fluctuation information, demand performance degradation information, and energy storage aging information; adjust the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage regulation space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; perform optimization of multi-source collaborative bus voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space to obtain an optimal bus voltage management scheme, wherein optimization is performed by configuring weights according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; use the optimal bus voltage management scheme to manage and control the bus voltage of the microgrid.

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

[0008] This application obtains the demand information on the demand side and the power generation characteristic information on the power generation side in the microgrid, predicts the voltage fluctuation of the bus in the microgrid to obtain the bus voltage fluctuation information; obtains the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side in the microgrid, and respectively conducts a trial optimization of voltage stability control management on the bus voltage fluctuation information to obtain reactive power fluctuation information, demand performance degradation information, and energy storage aging information; adjusts the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage regulation space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; conducts an optimization of multi-source collaborative bus voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space to obtain an optimal bus voltage management plan, where the optimization is performed by configuring weights according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; and uses the optimal bus voltage management plan to manage and control the bus voltage of the microgrid. The present invention solves the technical problem that the existing technology lacks an effective coordination mechanism in the coordinated regulation of voltage and there is difficulty in accurately controlling voltage fluctuations. By collecting data on the demand side and the power generation side, predicting the bus voltage fluctuations, obtaining the reactive power compensation, demand adjustment, and energy storage regulation spaces, conducting a trial optimization of voltage stability management, obtaining reactive power fluctuations, demand performance degradation, and energy storage aging information, adjusting the regulation space according to this information and conducting multi-source collaborative optimization, an optimal bus voltage management plan is obtained, achieving the technical effect of accurately controlling the bus voltage of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following described drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0010] Figure 1 It is a schematic flowchart of the method for managing the bus voltage of a microgrid based on multi-source collaborative optimization provided by an embodiment of this application;

[0011] Figure 2 It is a schematic flowchart of conducting a trial optimization of voltage stability control management in the method for managing the bus voltage of a microgrid based on multi-source collaborative optimization provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] The present application provides a microgrid bus voltage management method based on multi-source collaborative optimization, which is used to solve the technical problem that the existing technology lacks an effective coordination mechanism in the coordinated regulation of voltage and there is difficulty in accurately controlling voltage fluctuations. By collecting data on the demand side and the power generation side, predicting the bus voltage fluctuations, obtaining the reactive power compensation, demand adjustment, and energy storage regulation spaces, performing a trial optimization of voltage stability management, obtaining information on reactive power fluctuations, demand performance degradation, and energy storage aging, adjusting the regulation space according to this information, and performing multi-source collaborative optimization, an optimal bus voltage management scheme is obtained, achieving the technical effect of accurately controlling the microgrid bus voltage.

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0014] It should be noted that any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0015] As Figure 1 shown, the present application provides a microgrid bus voltage management method based on multi-source collaborative optimization, and the method includes:

[0016] Step S100: Obtain the demand information on the demand side in the microgrid and the power generation characteristic information on the power generation side, and perform a voltage fluctuation prediction of the bus in the microgrid to obtain bus voltage fluctuation information.

[0017] In the embodiments of the present application, in order to obtain the demand information on the demand side in the microgrid, power consumption data is collected through smart meters or load monitoring devices installed at power consumption nodes, and the collected data is used as the demand information on the demand side in the microgrid. In order to obtain the power generation characteristic information on the power generation side, parameters such as power generation power and power generation fluctuations are recorded through the monitoring systems of power generation units, such as photovoltaic and wind power, to obtain the power generation characteristic information on the power generation side.

[0018] Subsequently, based on the collected demand information and power generation characteristic information, using a voltage fluctuation prediction model trained with historical data, combined with time series analysis or machine learning algorithms, the bus voltage fluctuations in the next period of time are predicted to obtain bus voltage fluctuation information.

[0019] Further, in the method provided by the application embodiment, to obtain the demand information of the demand side in the microgrid and the power generation characteristic information of the power generation side, and perform voltage fluctuation prediction on the busbars in the microgrid, it further includes:

[0020] Obtain a sequence of demand information of the demand side in the microgrid within a preset time range in the past; obtain the power generation characteristic information of the power generation side in the microgrid within the preset time range in the past to obtain a sequence of power generation characteristic information; combine the sequence of demand information and the sequence of power generation characteristic information to perform voltage fluctuation prediction on the busbars in the microgrid and obtain busbar voltage fluctuation information.

[0021] In the application embodiment, first, the smart meters or load monitoring devices installed at each node on the demand side of the microgrid are used to collect the electricity consumption data within a preset time range in the past, and information such as the time series change of electricity consumption, peak and trough demands is obtained. The collected data is integrated in chronological order to form a sequence of demand information. At the same time, the power generation characteristic information within the same time range, including power generation power, power generation fluctuation, meteorological conditions and other power generation characteristics, is obtained from the power generation units on the power generation side, such as photovoltaic and wind power. These information are sorted in chronological order to form a sequence of power generation characteristic information.

[0022] According to the obtained sequence of demand information and sequence of power generation characteristic information, use the voltage fluctuation prediction model trained with historical data to perform voltage fluctuation prediction on the busbar voltage in the microgrid and obtain busbar voltage fluctuation information.

[0023] Further, in the method provided by the application embodiment, to combine the sequence of demand information and the sequence of power generation characteristic information to perform voltage fluctuation prediction on the busbars in the microgrid, it further includes:

[0024] According to the historical operation data of the microgrid, collect a set of sample demand information sequences, a set of sample power generation characteristic information sequences, and record the fluctuation data of the busbar voltage to obtain a set of sample busbar voltage fluctuation information; use the set of sample demand information sequences, the set of sample power generation characteristic information sequences and the set of sample busbar voltage fluctuation information to train a busbar voltage fluctuation predictor; use the busbar voltage fluctuation predictor to perform voltage fluctuation prediction on the sequence of demand information and the sequence of power generation characteristic information to obtain busbar voltage fluctuation information.

[0025] In the embodiments of the present application, historical operation data of the microgrid is obtained from the historical database of the microgrid. Then, through the historical operation data of the microgrid, a set of sample demand information sequences, a set of sample power generation characteristic information sequences, and a set of sample bus voltage fluctuation information are collected. The collected set of sample demand information sequences, set of sample power generation characteristic information sequences, and set of sample bus voltage fluctuation information are corresponding in time. Among them, the set of sample demand information sequences records the power consumption load change data on the demand side over a period of time; the set of sample power generation characteristic information sequences is a data sequence of the power generation characteristics on the power generation side, including data such as the power generation power and fluctuation characteristics of power generation equipment such as photovoltaic and wind power in the same time period; the set of sample bus voltage fluctuation information records the fluctuation of the microgrid bus voltage over time and is obtained by recording the fluctuation data of the bus voltage.

[0026] Next, use the aforementioned obtained set of sample demand information sequences, set of sample power generation characteristic information sequences, and set of sample bus voltage fluctuation information to train the bus voltage fluctuation predictor. By using a long short-term memory network for training, input the set of sample demand information sequences, set of sample power generation characteristic information sequences, and set of sample bus voltage fluctuation information into the model simultaneously to learn the influence mode of demand fluctuations and power generation fluctuations on bus voltage fluctuations. During the training process, gradually adjust the parameters according to the input demand and power generation characteristic information to minimize the prediction error, and finally form a bus voltage fluctuation predictor that can accurately predict future voltage fluctuations.

[0027] Finally, input the demand information sequence and the power generation characteristic information sequence into the trained bus voltage fluctuation predictor to predict the voltage fluctuation of the bus in the microgrid and obtain the bus voltage fluctuation information.

[0028] Step S200: Obtain the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side of the microgrid, and respectively perform trial optimization of voltage stability control management on the bus voltage fluctuation information to obtain reactive power fluctuation information, demand performance degradation information, and energy storage aging information.

[0029] In the embodiments of the present application, first obtain the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side. These spaces are preset. Among them, the reactive power compensation adjustment space refers to the range in which power generation equipment affects the bus voltage by adjusting reactive power; the demand adjustment space refers to the ability to reduce or increase power through peak shaving and valley filling or load adjustment, thereby regulating voltage fluctuations; the energy storage voltage regulation space is the range of the ability to regulate voltage through charge and discharge.

[0030] Next, conduct a trial optimization of voltage stability control management for the bus voltage fluctuation information. By simulating different adjustment schemes, evaluate the effect on voltage stability to find the optimal adjustment combination. First, perform reactive power adjustment within the reactive power compensation adjustment space. By adjusting the reactive power of the power generation equipment, analyze its impact on the bus voltage fluctuation and generate reactive power fluctuation information, that is, the specific impact of reactive power on voltage fluctuation. Then, simulate different load regulations within the demand adjustment space. By adjusting the load demand on the demand side, evaluate its impact on the voltage fluctuation stability and obtain the demand performance degradation information, that is, the degree of performance degradation on the demand side after load regulation. Finally, within the energy storage voltage regulation space, analyze the compensation effect of the energy storage on voltage fluctuation by adjusting the charge-discharge strategy and obtain the energy storage aging information.

[0031] Further, as Figure 2 shown, in the method provided by the application embodiment, obtaining the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side within the microgrid, and respectively conducting a trial optimization of voltage stability control management for the bus voltage fluctuation information further includes:

[0032] Obtain the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side within the microgrid; based on the bus voltage fluctuation information, conduct a trial optimization of the reactive power voltage management parameters within the reactive power compensation adjustment space to obtain the optimal reactive power voltage management parameters, and in combination with the power generation characteristic information on the power generation side, analyze and obtain the reactive power fluctuation information; conduct a trial optimization of the demand voltage management parameters within the demand adjustment space to obtain the optimal demand voltage management parameters, and obtain the power consumption performance degradation amplitude on the demand side under the optimal demand voltage management parameters as the demand performance degradation information; conduct a trial optimization of the energy storage voltage management parameters within the energy storage voltage regulation space to obtain the optimal energy storage voltage management parameters, and obtain the aging information on the energy storage side under the optimal energy storage voltage management parameters as the energy storage aging information.

[0033] In the embodiment of the present application, first, obtain the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side within the microgrid. Next, based on the bus voltage fluctuation information, perform a trial optimization of the reactive power voltage management parameters within the reactive power compensation adjustment space. The trial optimization refers to performing a limited number of simulations, such as 10 times, within the reactive power compensation adjustment space. By gradually adjusting the reactive power voltage management parameters, evaluate the impact of each simulation on the bus voltage and obtain multiple voltage fluctuation results. Specifically, within the reactive power compensation adjustment space, generate multiple reactive power adjustment parameters, such as 10 groups, and simulate their fluctuation effects on the bus voltage one by one. In each simulation, based on a simulation tool, such as MATLAB, obtain the voltage fluctuation information under each group of reactive power adjustment parameters, that is, the fluctuation amplitude and change trend of the voltage. For the results of each simulation, record their respective voltage fluctuation information and compare the amplitudes of the bus voltage fluctuations under different reactive power adjustment parameters. Evaluate the stability of the voltage fluctuation according to the standard deviation of the voltage fluctuation or the maximum voltage deviation. From multiple simulations, select a group of reactive power adjustment parameters with the smallest voltage fluctuation as the optimal reactive power voltage management parameters. At the same time, combine the power generation characteristic information on the power generation side, analyze the compensation effect of reactive power regulation on voltage fluctuation, and generate reactive power fluctuation information. Specifically, according to the characteristic information on the power generation side, simulate the power output characteristics of the power generation equipment, apply the previously determined optimal reactive power voltage management parameters in the simulation, and during the simulation process, record the voltage change situation after reactive power regulation as the power generation characteristics fluctuate. Through these simulation results, obtain the reactive power fluctuation information.

[0034] Similar to the aforementioned process of obtaining reactive power fluctuation information, to obtain the demand performance degradation information, first, randomly generate initial demand voltage management parameters within the demand adjustment space, and these parameters determine the load adjustment strategy on the demand side. Use a simulation tool to simulate the impact of each group of load adjustment strategies on the bus voltage, and by evaluating the voltage fluctuation amplitude under each group of parameters, select the demand voltage management parameters with the smallest voltage fluctuation as the optimal demand voltage management parameters. Next, according to the optimal demand voltage management parameters, evaluate the impact of the corresponding load adjustment strategy on the performance of the equipment on the demand side, and calculate the degree of degradation of the power consumption performance, such as the proportion of the degradation of the electrical appliance performance. Record the impact on the equipment performance on the demand side under the optimal demand voltage management parameters and generate the demand performance degradation information.

[0035] Similarly, within the energy storage voltage regulation space, randomly generate multiple groups of energy storage voltage management parameters to control the charge and discharge strategy of the energy storage equipment. Use a simulation tool to simulate the impact of each group of energy storage voltage management parameters on the bus voltage fluctuation, and select the strategy with the smallest voltage fluctuation as the optimal energy storage voltage management parameters. Evaluate the aging rate of the energy storage equipment under the optimal energy storage voltage management parameters and calculate the aging information of the battery, such as the battery capacity decay rate. Record the aging situation of the energy storage equipment under the optimal energy storage voltage management parameters and generate the energy storage aging information.

[0036] Further, in the method provided by the application embodiment, trial optimization of reactive voltage management parameters is performed within the reactive power compensation adjustment space to obtain optimal reactive voltage management parameters. Combining the power generation characteristic information of the power generation side, reactive power fluctuation information is analyzed and obtained, and it further includes:

[0037] Randomly generate a first reactive voltage management parameter within the reactive power compensation adjustment space, and obtain the first reactive management bus voltage for bus voltage management under the bus voltage fluctuation information of the first reactive voltage management parameter; calculate a first reactive voltage management fitness according to the difference between the first reactive management bus voltage and the bus stable voltage, where the magnitude of the first reactive voltage management fitness is negatively correlated with the magnitude of the difference; continue to perform trial optimization of reactive voltage management parameters within the reactive power compensation adjustment space until the trial optimization round is reached, and output the optimal reactive voltage management parameter with the maximum reactive voltage management fitness; combine the optimal reactive voltage management parameter and the power generation characteristic information sequence of the power generation side to analyze and obtain reactive power fluctuation information, where a sample reactive voltage management parameter set, a sample power generation characteristic information sequence set, and a sample reactive power fluctuation information set are collected to train a reactive power fluctuation analyzer, and reactive power fluctuation analysis is performed on the power generation characteristic information sequence and the optimal reactive voltage management parameter to obtain reactive power fluctuation information.

[0038] In the embodiment of the present application, first, within the reactive power compensation adjustment space, a random number generation algorithm is used to randomly generate multiple groups of initial reactive voltage management parameters, and each group of parameters represents a different reactive power regulation strategy. Next, a power system simulation tool, such as MATLAB, is used to apply the first reactive voltage management parameter to the simulation of the bus voltage fluctuation information. Through the simulation, the regulation effect of reactive power compensation on the bus voltage fluctuation under this specific parameter is calculated to obtain the first reactive management bus voltage.

[0039] After that, the difference between the first reactive management bus voltage and the bus stable voltage is determined, and then the first reactive voltage management fitness is calculated. The bus stable voltage is the voltage in the ideal state and is preset. When calculating the fitness, the reciprocal of the absolute value of the difference between the first reactive management bus voltage and the bus stable voltage is used as the first reactive voltage management fitness.

[0040] Subsequently, continue to perform trial optimization of the reactive power voltage management parameters within the reactive power compensation adjustment space until the number of trial optimization rounds is reached. Specifically, within the reactive power compensation adjustment space, use a random number generation algorithm to continue randomly generating reactive power voltage management parameters. In each round of trial optimization, a new set of reactive power voltage management parameters is randomly generated, where the number of trial optimization rounds is preset. For each randomly generated set of reactive power voltage management parameters, use a power system simulation tool, such as MATLAB, to simulate and calculate the bus voltage, and obtain the bus voltage fluctuation information under each set of parameters. According to the simulation results of each randomly generated set of parameters, calculate the fitness of this set of parameters. Record the simulation results of each set of parameters through each round of trial optimization, including the voltage fluctuation amplitude and the fitness value. After randomly generating parameters multiple times and performing simulations, compare the fitness of all parameters, and select the parameters that minimize the voltage fluctuation as the optimal reactive power voltage management parameters.

[0041] Finally, obtain the sample reactive power voltage management parameter set, the sample power generation characteristic information sequence set, and the sample reactive power fluctuation information set from the historical database. Each set of reactive power voltage management parameters corresponds to a set of power generation characteristic information sequences, and there is the corresponding reactive power fluctuation information for this combination. Based on the collected sample data, use a machine learning algorithm, such as a support vector machine, to train the reactive power fluctuation analyzer. Specifically, input the sample reactive power voltage management parameters, the power generation characteristic information sequence, and the reactive power fluctuation information into the machine learning model to construct a training set. Use the machine learning algorithm to train the reactive power fluctuation analyzer to learn the relationship between the power generation characteristic information and the reactive power regulation effect. Obtain the reactive power fluctuation analyzer through training. Input the optimal reactive power voltage management parameters and the power generation characteristic information sequence on the power generation side into the reactive power fluctuation analyzer for prediction to obtain the reactive power fluctuation information.

[0042] Step S300: Adjust the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage regulation space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information.

[0043] In the embodiment of the present application, first, calculate the adjustment ratio of each adjustment space based on the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information. By comparing the current information with the preset reactive power fluctuation standard, demand performance standard, and energy storage aging standard, calculate the deviations of reactive power compensation, demand adjustment, and energy storage adjustment, and generate the corresponding adjustment ratios. Adjust the reactive power compensation adjustment space according to the reactive power adjustment ratio generated from the reactive power fluctuation information, adjust the demand adjustment space according to the demand adjustment ratio generated from the demand performance degradation information, and adjust the energy storage voltage regulation space according to the energy storage adjustment ratio generated from the energy storage aging information.

[0044] Further, in the method provided by the application embodiment, adjusting the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage adjustment space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information further includes:

[0045] Calculating the ratio of the preset reactive power fluctuation information to the reactive power fluctuation information, calculating the ratio of the preset demand performance degradation information to the demand performance degradation information, and calculating the ratio of the preset energy storage aging information to the energy storage aging information, and performing ratio normalization processing to obtain a reactive power adjustment ratio, a demand adjustment ratio, and an energy storage adjustment ratio; using the reactive power adjustment ratio, the demand adjustment ratio, and the energy storage adjustment ratio to perform adjustment calculations on the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage adjustment space, and retaining the reactive power compensation adjustment interval, the demand adjustment interval, and the energy storage voltage adjustment interval of the previous reactive power adjustment ratio, demand adjustment ratio, and energy storage adjustment ratio to obtain the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space.

[0046] In the embodiment of the present application, first, the obtained reactive power fluctuation information, demand performance degradation information, and energy storage aging information are compared with preset standard values. The preset standard values are respectively preset reactive power fluctuation information, preset demand performance degradation information, and preset energy storage aging information. By comparison, a reactive power fluctuation ratio, a demand performance degradation ratio, and an energy storage aging ratio are obtained. Next, a normalization algorithm, such as Min - Max normalization, is used to normalize each ratio to a unified range to obtain a reactive power adjustment ratio, a demand adjustment ratio, and an energy storage adjustment ratio, and the sum of the reactive power adjustment ratio, the demand adjustment ratio, and the energy storage adjustment ratio is 1.

[0047] Next, the reactive power adjustment ratio, the demand adjustment ratio, and the energy storage adjustment ratio are used to perform reduction calculations on their respective adjustment spaces. Specifically, according to the reactive power adjustment ratio, the adjustment parameters in the reactive power compensation adjustment space are reduced proportionally; according to the demand adjustment ratio, the range of the demand - side load adjustment parameters is reduced, such as the amplitude of load peak shaving and valley filling; according to the energy storage adjustment ratio, the charge - discharge parameter range of the energy storage device is reduced. For example, assuming that the original demand adjustment space is 0 - 200 KW, if the demand adjustment ratio is 30%, then the adjusted demand adjustment interval is reduced to 0 - 60 KW. Similarly, the reactive power compensation adjustment space and the energy storage voltage adjustment space also reduce their respective adjustment parameter ranges proportionally according to the reactive power adjustment ratio and the energy storage adjustment ratio.

[0048] After adjusting their respective adjustment parameters, according to the previous reactive power adjustment ratio, demand adjustment ratio, and energy storage adjustment ratio, retain the reactive power compensation adjustment interval, demand adjustment interval, and energy storage voltage adjustment interval corresponding to the previous reactive power adjustment ratio, demand adjustment ratio, and energy storage adjustment ratio, and finally obtain the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space.

[0049] Step S400: Optimize the multi-source collaborative bus voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space to obtain the optimal bus voltage management plan, where the optimization is performed by configuring weights according to the reactive power fluctuation information, demand performance degradation information, and energy storage aging information.

[0050] In the embodiment of the present application, first, a first bus voltage management plan is randomly generated, which includes reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters. Then, weights are configured according to the reactive power fluctuation information, demand performance degradation information, and energy storage aging information, and the multi-source management fitness of this plan is calculated. The fitness reflects the effect of this adjustment strategy in balancing voltage fluctuations, demand-side performance, and energy storage device aging. Next, through multiple rounds of collaborative optimization, within the adjusted reactive power compensation, demand adjustment, and energy storage voltage adjustment spaces, new bus voltage management plans are continuously generated and their fitness is calculated. The optimization process continues until the fitness reaches a convergence state, and finally, the bus voltage management plan with the highest fitness is output, that is, the optimal bus voltage management plan. This plan comprehensively considers the weight configuration of reactive power fluctuations, load demands, and energy storage device aging, and realizes voltage stability, minimum loss of equipment performance, and maximization of the life of energy storage devices.

[0051] Furthermore, in the method provided by the embodiment of the application, when optimizing the multi-source collaborative bus voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space, it further includes:

[0052] Randomly generate a first bus voltage management plan within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space, where the first bus voltage management plan includes reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters; according to the first bus voltage management plan, analyze and calculate to obtain the first multi-source management fitness, where the calculation is performed by configuring weights according to the reactive power fluctuation information, demand performance degradation information, and energy storage aging information; continue to optimize the multi-source collaborative bus voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage adjustment space until convergence, output the bus voltage management plan with the maximum multi-source management fitness, and obtain the optimal bus voltage management plan.

[0053] In the embodiment of the present application, first, within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space, a random number generation algorithm is used to randomly generate a first bus voltage management scheme. This scheme includes reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters.

[0054] After generating the first bus voltage management scheme, by analyzing the effect of this scheme, its multi-source management fitness is calculated. When calculating the fitness, first, the matching situation between the reactive power voltage management parameters and the reactive power fluctuation information is evaluated, and weights are configured according to the magnitude of the reactive power fluctuation. The larger the weight, the more important the reactive power regulation effect. Then, the load peak shaving and valley filling effect is evaluated based on the demand voltage management parameters, considering the impact of this strategy on the performance of the demand-side equipment. The greater the performance degradation, the lower the fitness. Finally, the energy storage voltage management parameters are used to evaluate the aging condition of the energy storage equipment. If this scheme accelerates the aging of the energy storage equipment, the fitness is lower. By comprehensively analyzing this information, the multi-source management fitness of this scheme is calculated based on the weights. The higher the fitness, the better the performance of the scheme in terms of voltage stability, load adjustment, and energy storage management.

[0055] Next, optimization continues based on this fitness. Within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space, new bus voltage management schemes are continuously randomly generated, and the multi-source management fitness of each scheme is calculated in the simulation environment. Through multiple rounds of iteration, the fitness of each scheme is gradually optimized, and the combined strategies of reactive power compensation, demand adjustment, and energy storage regulation are continuously improved. In each iteration, the fitness of the newly generated scheme is compared with that of the previous scheme, and the scheme with the higher fitness is retained. This optimization process continues until the fitness value tends to be stable, that is, the convergence state, indicating that the optimal bus voltage management scheme is obtained. After convergence, the scheme with the highest multi-source management fitness is output as the optimal bus voltage management scheme.

[0056] Furthermore, in the method provided by the application embodiment, according to the first bus voltage management scheme, analyzing and calculating to obtain the first multi-source management fitness further includes:

[0057] Based on historical microgrid bus voltage management data, collect a set of sample bus voltage management schemes, and obtain a multi-source management bus voltage set after voltage management of different sample bus voltage management schemes in the bus voltage fluctuation information; use the set of sample bus voltage management schemes and the multi-source management bus voltage set to train a multi-source management voltage predictor to perform multi-source management voltage prediction on the first bus voltage management scheme to obtain a first multi-source management bus voltage; according to the reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters in the first bus voltage management scheme, analyze and obtain a first reactive power fluctuation information, a first demand performance degradation information, and a first energy storage aging information; according to the first multi-source management bus voltage, the first reactive power fluctuation information, the first demand performance degradation information, and the first energy storage aging information, calculate and obtain a first multi-source management fitness, as shown in the following formula:

[0058]

[0059] where, MBV is the first multi-source management fitness, w1, w2, w3, and w4 are weights, V W is the bus stable voltage, V D is the multi-source management bus voltage, VAR y is the preset reactive power fluctuation information, VAR b is the first reactive power fluctuation information, X y is the preset demand performance degradation information, X u is the first demand performance degradation information, L y is the preset energy storage aging information, L u is the first energy storage aging information, and the ratio of w2, w3, and w4 is the same as the ratio of the reactive power adjustment ratio, the demand adjustment ratio, and the energy storage adjustment ratio.

[0060] In the embodiments of the present application, first, based on historical microgrid bus voltage management data, basic data is obtained through the historical data collection and sample set establishment step, including historical parameters of reactive power compensation, load management, and energy storage regulation, as well as the bus voltage fluctuation conditions under these management strategies. Through these historical data, a set of sample bus voltage management schemes is established to form a complete multi-source management bus voltage set. Next, a machine learning model, such as a support vector machine, is used to predict future voltage fluctuation conditions. During the training process, the input data includes historical management parameters of reactive power compensation, demand adjustment, and energy storage regulation, and the output is the voltage fluctuation prediction under these management schemes. Through training, a multi-source management voltage predictor is obtained.

[0061] Next, the first bus voltage management scheme is input into the multi-source management voltage predictor for multi-source management voltage prediction to obtain the first multi-source management bus voltage. Then, according to the reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters in the first bus voltage management scheme, through the same process as described above, the first reactive power fluctuation information, the first demand performance degradation information, and the first energy storage aging information are analyzed and obtained. Then, according to the obtained first multi-source management bus voltage, the first reactive power fluctuation information, the first demand performance degradation information, and the first energy storage aging information, the first multi-source management fitness is calculated through the given fitness formula. In this fitness formula, w1 is a preset weight, the ratio of w2, w3, and w4 is the same as the ratio of the reactive power adjustment ratio, demand adjustment ratio, and energy storage adjustment ratio, and the sum of w2, w3, and w4 is the value of 1 minus w1.

[0062] Through the above steps, the first multi-source management fitness is obtained.

[0063] Step S500: Use the optimal bus voltage management scheme to manage and control the bus voltage of the microgrid.

[0064] In the embodiment of the present application, according to the reactive power voltage management parameters in the optimal bus voltage management scheme, the reactive power is adjusted, and reactive power compensation equipment is used to ensure the stability of the grid voltage. Then, through the demand voltage management parameters, the peak shaving and valley filling strategy is executed to adjust the load demand, relieve the peak load pressure, and ensure the stable power supply of the grid. At the same time, according to the energy storage voltage management parameters, the charge and discharge strategy of the energy storage device is adjusted, so that the energy storage device can respond quickly during voltage fluctuations and extend its service life. Through this process, the management and control of the bus voltage of the microgrid are completed.

[0065] In the embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects:

[0066] This application obtains the demand information on the demand side and the power generation characteristic information on the power generation side in the microgrid, predicts the voltage fluctuations of the busbars in the microgrid to obtain the busbar voltage fluctuation information; obtains the reactive power compensation adjustment space on the power generation side, the demand adjustment space on the demand side, and the energy storage voltage regulation space on the energy storage side in the microgrid, and respectively performs a trial optimization of voltage stability control management on the busbar voltage fluctuation information to obtain reactive power fluctuation information, demand performance degradation information, and energy storage aging information; adjusts the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage regulation space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; performs an optimization of multi-source collaborative busbar voltage management within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space to obtain an optimal busbar voltage management plan, wherein the optimization is performed by configuring weights according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; and uses the optimal busbar voltage management plan to manage and control the busbar voltage of the microgrid. The present invention solves the technical problem that the prior art lacks an effective coordination mechanism in the coordinated regulation of voltage and there is difficulty in accurately controlling voltage fluctuations. By collecting data on the demand side and the power generation side, predicting the busbar voltage fluctuations, obtaining the reactive power compensation, demand adjustment, and energy storage regulation spaces, performing a trial optimization of voltage stability management, obtaining the reactive power fluctuation, demand performance degradation, and energy storage aging information, adjusting the regulation space according to this information and performing multi-source collaborative optimization, an optimal busbar voltage management plan is obtained, achieving the technical effect of accurately controlling the busbar voltage of the microgrid.

[0067] It should be noted that the above sequence of embodiments of this application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. The processes depicted in the drawings do not necessarily require the specific sequence and continuous sequence shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0068] The above are only the preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

[0069] This specification and the drawings are only exemplary descriptions of this application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art can make various changes and modifications to this application without departing from the scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application is intended to include these changes and modifications.

Claims

1. A microgrid bus voltage management method based on multi-source collaborative optimization, characterized in that, The method includes: Obtaining the demand information of the demand side in the microgrid and the power generation characteristic information of the power generation side, predicting the voltage fluctuation of the bus in the microgrid, and obtaining the bus voltage fluctuation information; Obtaining the reactive power compensation adjustment space of the power generation side, the demand adjustment space of the demand side, and the energy storage voltage regulation space of the energy storage side in the microgrid, respectively performing trial optimization of voltage stability control management on the bus voltage fluctuation information, and obtaining reactive power fluctuation information, demand performance degradation information, and energy storage aging information; Adjusting the reactive power compensation adjustment space, the demand adjustment space, and the energy storage voltage regulation space according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; In the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space, performing optimization of multi-source collaborative bus voltage management to obtain an optimal bus voltage management scheme, wherein the optimization is performed by configuring weights according to the reactive power fluctuation information, the demand performance degradation information, and the energy storage aging information; Using the optimal bus voltage management scheme to manage and control the bus voltage of the microgrid.

2. The microgrid bus voltage management method based on multi-source collaborative optimization according to claim 1, wherein Obtaining the demand information of the demand side in the microgrid and the power generation characteristic information of the power generation side, and predicting the voltage fluctuation of the bus in the microgrid, including: Obtaining the demand information sequence of the demand side in the microgrid within a past preset time range; Obtaining the power generation characteristic information of the power generation side in the microgrid within the past preset time range to obtain a power generation characteristic information sequence; Combining the demand information sequence and the power generation characteristic information sequence to predict the voltage fluctuation of the bus in the microgrid and obtaining the bus voltage fluctuation information.

3. The microgrid bus voltage management method based on multi-source collaborative optimization according to claim 2, wherein Combining the demand information sequence and the power generation characteristic information sequence to predict the voltage fluctuation of the bus in the microgrid, including: According to the historical operation data of the microgrid, collecting a sample demand information sequence set, a sample power generation characteristic information sequence set, and recording the fluctuation data of the bus voltage to obtain a sample bus voltage fluctuation information set; Using the sample demand information sequence set, the sample power generation characteristic information sequence set, and the sample bus voltage fluctuation information set to train a bus voltage fluctuation predictor; Using the bus voltage fluctuation predictor to predict the voltage fluctuation of the bus in the microgrid for the demand information sequence and the power generation characteristic information sequence, and obtaining the bus voltage fluctuation information.

4. The microgrid bus voltage management method based on multi-source collaborative optimization according to claim 1, characterized in that Obtaining the reactive power compensation adjustment space of the power generation side, the demand adjustment space of the demand side, and the energy storage voltage regulation space of the energy storage side in the microgrid, and respectively performing trial optimization of voltage stability control management on the bus voltage fluctuation information, including: Obtaining the reactive power compensation adjustment space of the power generation side, the demand adjustment space of the demand side, and the energy storage voltage regulation space of the energy storage side in the microgrid; Based on the bus voltage fluctuation information, performing trial optimization of reactive power voltage management parameters in the reactive power compensation adjustment space to obtain optimal reactive power voltage management parameters, and analyzing and obtaining reactive power fluctuation information in combination with the power generation characteristic information of the power generation side; Perform trial optimization of the demand voltage management parameters within the demand adjustment space to obtain the optimal demand voltage management parameters, and obtain the reduction amplitude of the power consumption performance on the demand side under the optimal demand voltage management parameters as the demand performance degradation information; Perform trial optimization of the energy storage voltage management parameters within the energy storage voltage regulation space to obtain the optimal energy storage voltage management parameters, and obtain the aging information on the energy storage side under the optimal energy storage voltage management parameters as the energy storage aging information.

5. The microgrid bus voltage management method based on multi-source collaborative optimization according to claim 4, characterized in that Perform trial optimization of the reactive power voltage management parameters within the reactive power compensation adjustment space to obtain the optimal reactive power voltage management parameters, and analyze and obtain the reactive power fluctuation information in combination with the power generation characteristic information of the power generation side, including: Randomly generate the first reactive power voltage management parameters within the reactive power compensation adjustment space, and obtain the first reactive power management bus voltage for bus voltage management under the bus voltage fluctuation information of the first reactive power voltage management parameters; Calculate the first reactive power voltage management fitness according to the difference between the first reactive power management bus voltage and the bus stable voltage, where the magnitude of the first reactive power voltage management fitness is negatively correlated with the magnitude of the difference; Continue to perform trial optimization of the reactive power voltage management parameters within the reactive power compensation adjustment space until the trial optimization round is reached, and output the optimal reactive power voltage management parameters with the maximum reactive power voltage management fitness; Analyze and obtain the reactive power fluctuation information in combination with the optimal reactive power voltage management parameters and the power generation characteristic information sequence of the power generation side. Among them, collect the sample reactive power voltage management parameter set, the sample power generation characteristic information sequence set and the sample reactive power fluctuation information set, train the reactive power fluctuation analyzer, and perform reactive power fluctuation analysis on the power generation characteristic information sequence and the optimal reactive power voltage management parameters to obtain the reactive power fluctuation information.

6. The method for managing the bus voltage of a microgrid based on multi-source collaborative optimization according to claim 1, characterized in that According to the reactive power fluctuation information, demand performance degradation information and energy storage aging information, adjust the reactive power compensation adjustment space, demand adjustment space and energy storage voltage regulation space, including: Calculate the ratio of the preset reactive power fluctuation information to the reactive power fluctuation information, calculate the ratio of the preset demand performance degradation information to the demand performance degradation information, and calculate the ratio of the preset energy storage aging information to the energy storage aging information, and perform ratio normalization processing to obtain the reactive power adjustment ratio, demand adjustment ratio and energy storage adjustment ratio; Use the reactive power adjustment ratio, demand adjustment ratio and energy storage adjustment ratio to perform adjustment calculations on the reactive power compensation adjustment space, demand adjustment space and energy storage voltage regulation space, and retain the reactive power compensation adjustment interval, demand adjustment interval and energy storage voltage regulation interval of the previous reactive power adjustment ratio, demand adjustment ratio and energy storage adjustment ratio to obtain the adjusted reactive power compensation adjustment space, demand adjustment space and energy storage voltage regulation space.

7. The microgrid bus voltage management method based on multi-source collaborative optimization according to claim 6, characterized in that Within the adjusted reactive power compensation adjustment space, demand adjustment space and energy storage voltage regulation space, perform optimization of multi-source collaborative bus voltage management, including: Randomly generate a first bus voltage management plan within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space. The first bus voltage management plan includes reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters; According to the first bus voltage management plan, analyze and calculate to obtain the first multi-source management fitness, where the calculation is performed by configuring weights according to the reactive power fluctuation information, demand performance degradation information, and energy storage aging information; Continue to optimize the bus voltage management with multi-source coordination within the adjusted reactive power compensation adjustment space, demand adjustment space, and energy storage voltage regulation space until convergence, and output the bus voltage management plan with the maximum multi-source management fitness to obtain the optimal bus voltage management plan.

8. The method for managing the bus voltage of a microgrid based on multi-source collaborative optimization according to claim 7, wherein According to the first bus voltage management plan, analyze and calculate to obtain the first multi-source management fitness, including: Based on the historical microgrid bus voltage management data, collect a set of sample bus voltage management plans, and obtain a set of multi-source management bus voltages after voltage management of different sample bus voltage management plans in the bus voltage fluctuation information; Use the set of sample bus voltage management plans and the set of multi-source management bus voltages to train a multi-source management voltage predictor, perform multi-source management voltage prediction on the first bus voltage management plan, and obtain the first multi-source management bus voltage; According to the reactive power voltage management parameters, demand voltage management parameters, and energy storage voltage management parameters in the first bus voltage management plan, analyze and obtain the first reactive power fluctuation information, the first demand performance degradation information, and the first energy storage aging information; According to the first multi-source management bus voltage, the first reactive power fluctuation information, the first demand performance degradation information, and the first energy storage aging information, calculate and obtain the first multi-source management fitness, as shown in the following formula: Among them, MBV is the first multi-source management fitness, w1, w2, w3, and w4 are weights, V W is the bus stable voltage, V D is the multi-source management bus voltage, VAR y is the preset reactive power fluctuation information, VAR b is the first reactive power fluctuation information, X y is the preset demand performance degradation information, X u is the first demand performance degradation information, L y is the preset energy storage aging information, L u is the first energy storage aging information. The ratio of w2, w3, and w4 is the same as the ratio of the reactive power adjustment ratio, the demand adjustment ratio, and the energy storage adjustment ratio.

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