A microgrid intelligent control method

Abstract

The present invention discloses a microgrid intelligent control method, which relates to the technical field of electric power grids. The method includes: real-time monitoring of the main grid in the target area according to the power sensing network to obtain the main grid monitoring data; obtaining the main grid fault prediction coefficient; if the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold, activating the first strategy for microgrid state switching and the first channel for microgrid control; establishing equipment power supply priority distribution; obtaining the first microgrid control scheme; and controlling the microgrid based on the first microgrid control scheme. The method solves the technical problem that the microgrid cannot be flexibly controlled according to the state of the main grid in the prior art, resulting in poor timeliness and low precision of microgrid control, and achieves the technical effect of flexibly controlling the microgrid according to the state of the main grid, improving the timeliness and precision of microgrid control, and ensuring the stability of power supply.

Description

Technical Field

[0001] The present application relates to the technical field related to electric power grids, and in particular to a method for intelligent control of microgrids. Background Art

[0002] With the transformation of the global energy structure and the continuous development of smart grid technology, microgrids, as an important integrated way of distributed energy, are gradually becoming one of the key technologies to ensure the stable operation of power systems and improve energy efficiency. Microgrids are a local energy network that integrates distributed power sources, energy storage devices, loads and control devices. They can be flexibly connected to the main grid and operate in two modes: connected to the main grid (grid connection) or disconnected from the main grid (island operation). They can achieve grid connection or island operation, thereby achieving efficient use of energy, providing stable and reliable power supply to local areas and supporting the access of renewable energy. Microgrids usually include the following key components: distributed energy, such as photovoltaic power generation, wind power generation, fuel cells and other small power generation equipment, energy storage systems, such as battery energy storage systems, used to balance supply and demand and maintain power stability, loads, such as industrial or household electrical equipment, and control systems, used to manage power flow, load balancing and interface with the main grid within the microgrid. However, how to achieve intelligent control of microgrids in a complex and changeable power environment, ensure that they can respond quickly when the main power grid fails, and ensure continuous power supply to key loads has become a hot topic and difficulty in current research. Traditional microgrid control often relies on post-processing, that is, countermeasures are taken only after the main power grid fails, which limits the overall fault response capability. In addition, it is impossible to ensure the reasonable allocation of power resources and ensure the power supply to key loads during the microgrid control process, while maintaining the economy and environmental protection of the microgrid, which in turn affects the stability and reliability of the entire microgrid.

[0003] At present, there is a technical problem in the grid control related technologies that the microgrid cannot be flexibly controlled according to the status of the main grid, resulting in poor timeliness and low accuracy of microgrid control. Summary of the invention

[0004] The present application provides a microgrid intelligent control method to solve the technical problem in the prior art that the microgrid cannot be flexibly controlled according to the status of the main power grid, resulting in poor timeliness and low accuracy of microgrid control. The application achieves the technical effect of flexibly controlling the microgrid according to the status of the main power grid, improving the timeliness and accuracy of microgrid control, and ensuring the stability of power supply.

[0005] The present application provides a microgrid intelligent control method, including: real-time monitoring of the main power grid in the target area according to the power sensing network to obtain main power grid monitoring data, wherein the microgrid in the target area is in a grid-connected operation state; inputting the main power grid monitoring data into the main power grid fault prediction integrated channel to obtain a main power grid fault prediction coefficient; judging whether the main power grid fault prediction coefficient is greater than / equal to the main power grid fault prediction threshold, if the main power grid fault prediction coefficient is greater than / equal to the main power grid fault prediction threshold, activating a first strategy for microgrid state switching and a first microgrid control channel; according to a power supply priority evaluation rule, performing power supply priority analysis on a load device set in the target area to establish a device power supply priority distribution; based on the device power supply priority distribution and the first microgrid control channel, deeply optimizing the first strategy for microgrid state switching to obtain a first microgrid control scheme; and controlling the microgrid based on the first microgrid control scheme.

[0006] In a possible implementation, the main grid monitoring data is input into a main grid fault prediction integrated channel to obtain a main grid fault prediction coefficient, and the following processing is also performed: the main grid fault prediction integrated channel includes a main grid fault probability prediction channel, a main grid fault risk prediction channel, a fault prediction normalization processing channel and a fault prediction weighted channel; the main grid monitoring data is input into the main grid fault probability prediction channel to obtain a main grid fault probability prediction coefficient; based on the main grid monitoring data and according to the main grid fault risk prediction channel, a main grid fault risk prediction coefficient is obtained; the main grid fault probability prediction coefficient and the main grid fault risk prediction coefficient are input into the fault prediction normalization processing channel to obtain a fault prediction normalization processing result; the fault prediction normalization processing result is input into the fault prediction weighted channel to generate the main grid fault prediction coefficient.

[0007] In a possible implementation, the power supply priority of the load equipment set in the target area is parsed, and the equipment power supply priority distribution is established. The following processing is also performed: the power supply priority evaluation rule includes a power supply priority evaluation multidimensional indicator and a power supply priority evaluation multidimensional weight, wherein the power supply priority evaluation multidimensional indicator includes equipment criticality, equipment power failure loss and equipment power redundancy, and the power supply priority evaluation multidimensional weight includes a predetermined weight coefficient corresponding to each indicator in the power supply priority evaluation multidimensional indicator; basic information is collected according to the load equipment set to obtain each equipment information set; based on the each equipment information set, the power supply priority of the load equipment set is evaluated according to the power supply priority evaluation multidimensional indicator to obtain each equipment evaluation coefficient set; normalization is performed according to the evaluation coefficient set of each equipment to obtain evaluation results of each equipment; weighted calculation is performed on the evaluation results of each equipment based on the power supply priority evaluation multidimensional weight to obtain multiple equipment power supply priority evaluation coefficients; the multiple equipment power supply priority evaluation coefficients are arranged in descending order to generate the equipment power supply priority distribution.

[0008] In a possible implementation, the first strategy for microgrid state switching is deeply optimized based on the device power supply priority distribution and the first microgrid control channel to obtain a first microgrid control plan, and the following processing is also performed: obtaining the predicted fault operation and maintenance duration of the main power grid; predicting the power supply capacity of the microgrid according to the predicted fault operation and maintenance duration to determine the predicted power supply capacity; based on the device power supply priority distribution, optimizing the load device set according to the predicted fault operation and maintenance duration and the predicted power supply capacity to determine the key load device set; based on the predicted fault operation and maintenance duration and the key load device set, generating a first microgrid control decision according to the first microgrid control channel; supplementing the first microgrid state switching strategy according to the first microgrid control decision to generate the first microgrid control plan.

[0009] In a possible implementation, the load device set is optimized according to the predicted duration of the fault operation and maintenance and the predicted power supply capacity to determine the critical load device set, and the following processing is also performed: the distribution capacity of the load device set is predicted according to the predicted duration of the fault operation and maintenance to determine the predicted distribution capacity of each device; the predicted distribution capacity of each device is summed up to obtain the total predicted distribution capacity; it is determined whether the total predicted distribution capacity is less than or equal to the predicted power supply capacity; if the total predicted distribution capacity is less than or equal to the predicted power supply capacity, the load device set is added to the critical load device set; if the total predicted distribution capacity is greater than the predicted power supply capacity, the difference between the total predicted distribution capacity and the predicted power supply capacity is calculated to determine the shortfall power supply capacity; based on the shortfall power supply capacity and the predicted distribution capacity of each device, the load device set is eliminated according to the power supply priority distribution of the equipment to generate the critical load device set.

[0010] In a possible implementation, based on the predicted fault operation and maintenance duration and the key load equipment set, a first microgrid control decision is generated according to the first microgrid control channel, and the following processing is also performed: based on the power sensing network, microgrid monitoring data of the microgrid is obtained; based on the predicted fault operation and maintenance duration, power supply demand analysis is performed on the key load equipment set to obtain the power supply demand distribution of key equipment; the microgrid monitoring data, the predicted fault operation and maintenance duration and the power supply demand distribution of key equipment are input into the first microgrid control channel to obtain the first microgrid control decision.

[0011] In a possible implementation, the microgrid is regulated based on the first microgrid regulation scheme, and the following processing is also performed: when the main grid returns to normal, the second microgrid state switching strategy and the second microgrid regulation channel are activated; the second microgrid state switching strategy is deeply optimized according to the second microgrid regulation channel to obtain the second microgrid regulation scheme; the microgrid is regulated based on the second microgrid regulation scheme.

[0012] In a possible implementation, the second strategy for switching the microgrid state is deeply optimized according to the second microgrid control channel to obtain a second microgrid control scheme, and the following processing is also performed: when the main grid returns to normal, the main grid update monitoring results and the microgrid update monitoring results are obtained according to the power sensing network; the power supply demand analysis is performed according to the target area to obtain the regional power supply demand; the main grid update monitoring results, the microgrid update monitoring results and the regional power supply demand are input into the second microgrid control channel to obtain a second microgrid control decision; the second strategy for switching the microgrid state is supplemented according to the second microgrid control decision to generate the second microgrid control scheme.

[0013] The present application proposes a method for intelligent microgrid control, which monitors the main grid of the target area in real time based on the power sensing network, obtains the main grid monitoring data, obtains the main grid fault prediction coefficient, activates the first strategy for microgrid state switching and the first channel for microgrid control if the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold, establishes equipment power supply priority distribution, obtains the first microgrid control scheme, and controls the microgrid based on the first microgrid control scheme. This solves the technical problem that the microgrid cannot be flexibly controlled according to the state of the main grid in the prior art, resulting in poor timeliness and low precision of microgrid control, and achieves the technical effect of flexibly controlling the microgrid according to the state of the main grid, improving the timeliness and precision of microgrid control, and ensuring the stability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a flow chart of a microgrid intelligent control method provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of a process for obtaining a main grid fault prediction coefficient in a microgrid intelligent control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] 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.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application embodiment provides a microgrid intelligent control method, such as Figure 1 As shown, the method includes:

[0021] Step S100, real-time monitoring of the main power grid in the target area is performed according to the power sensing network to obtain main power grid monitoring data, wherein the microgrid in the target area is in a grid-connected operation state.

[0022] Preferably, the microgrid in the target area is in a grid-connected operation state, which means that the microgrid is connected to the public large power grid (i.e., the main power grid) and exchanges electric energy with the main power distribution system. In this state, the microgrid can act as an electricity production and consumption unit, which can obtain electric energy from the main power grid and provide excess electric energy to the main power grid. Specifically, the main power grid in the target area is monitored in real time through the power sensing network to obtain monitoring data of the main power grid, including key parameters such as voltage, current, and power of the microgrid, which is used to understand the operating status of the main power grid, predict power demand, optimize power distribution, etc. Among them, the power sensing network is a network composed of various sensors, measuring equipment and data analysis systems, which is used to monitor the status and parameters of the power grid in real time; the characteristic of grid-connected operation is that two-way flow of electric energy can be achieved between the microgrid and the main grid. When the power generation within the microgrid is insufficient, electric energy can be obtained from the main grid to meet the load demand; when the power generation within the microgrid is in excess, the excess electric energy can be transmitted to the main grid; grid-connected operation contributes to the stable operation of the microgrid. Since it is connected to the main grid, the microgrid can use the regulation ability and stability of the main grid to cope with internal power fluctuations and load changes; grid-connected operation can improve the economy and efficiency of the microgrid. By optimizing power production and consumption, the microgrid can reduce operating costs and improve energy utilization efficiency.

[0023] Step S200: input the main power grid monitoring data into a main power grid fault prediction integrated channel to obtain a main power grid fault prediction coefficient.

[0024] Preferably, the main grid monitoring data (including but not limited to key parameters such as voltage, current, frequency, phase, etc.) is input into the main grid fault prediction integrated channel. The main grid fault prediction integrated channel is a model that integrates multiple data processing, analysis and prediction algorithms, which can receive the main grid monitoring data, and clean, integrate and standardize it to eliminate noise and outliers in the data, improve the accuracy and reliability of the data, and predict the fault of the grid, that is, by learning and analyzing the historical fault data, a prediction model is built based on a machine learning model (such as random forest, neural network, etc.), and trained with historical fault data, which can identify the pattern and characteristics of grid faults, and predict the possibility of future grid faults based on this, and finally output the main grid fault prediction coefficient, which reflects the degree of correlation between the current operation state of the grid and the fault risk. Generally, the higher the fault prediction coefficient, the greater the possibility of grid failure. Among them, the main grid fault prediction coefficient can help operation and maintenance personnel to timely understand the operation state and potential risks of the grid, so as to take corresponding preventive measures to avoid or reduce the impact of faults on the grid. By inputting the main grid monitoring data into the main grid fault prediction integrated channel and obtaining the fault prediction coefficient, accurate prediction and effective prevention of grid faults can be achieved, and the stability and reliability of the power system can be improved.

[0025] like Figure 2 As shown, step S200 further includes step S210, wherein the main grid fault prediction integrated channel includes a main grid fault probability prediction channel, a main grid fault risk prediction channel, a fault prediction normalization processing channel and a fault prediction weighted channel; step S220, inputting the main grid monitoring data into the main grid fault probability prediction channel to obtain a main grid fault probability prediction coefficient; step S230, based on the main grid monitoring data and according to the main grid fault risk prediction channel, obtaining a main grid fault risk prediction coefficient; step S240, inputting the main grid fault probability prediction coefficient and the main grid fault risk prediction coefficient into the fault prediction normalization processing channel to obtain a fault prediction normalization processing result; step S250, inputting the fault prediction normalization processing result into the fault prediction weighted channel to generate the main grid fault prediction coefficient.

[0026] Preferably, the main power grid fault prediction integrated channel includes a main power grid fault probability prediction channel, a main power grid fault risk prediction channel, a fault prediction normalization processing channel and a fault prediction weighted channel. Specifically, the main power grid fault probability prediction channel is mainly used to predict the probability of failure of the main power grid, and receives the main power grid monitoring data as input, including key parameters such as voltage, current, frequency, etc. of each node in the power grid, that is, a prediction model is constructed based on a machine learning model (such as a decision tree, random forest, neural network, etc.), and the prediction model is trained using the power grid fault history data as training data, so as to identify the mode and characteristics of the power grid fault, and obtain the main power grid fault probability prediction channel. The fault probability prediction channel can predict the possibility of future power grid failures based on these patterns and characteristics, and output a main power grid failure probability prediction coefficient, which reflects the probability of failure under the current state of the power grid; the main power grid fault risk prediction channel is similar to the fault probability prediction channel. The fault risk prediction channel also receives the main power grid monitoring data as input, and focuses on evaluating the risks and impacts that may be caused by the failure, including the scope, duration, economic losses, etc. of the power outage that may be caused by the failure. By applying risk assessment models and methods, the main power grid failure risk prediction coefficient is output, which reflects the size and severity of the risks that may be caused by the failure.

[0027] Preferably, since the fault probability prediction coefficient and the fault risk prediction coefficient may adopt different dimensions, a fault prediction normalization processing channel is used to perform normalization processing to ensure their comparability. The fault probability prediction coefficient and the fault risk prediction coefficient are received as input, and the main power grid fault probability prediction coefficient and the main power grid fault risk prediction coefficient are converted to a unified scale. The result after normalization processing can more accurately reflect the fault risk and probability of the power grid; after obtaining the normalized fault prediction coefficient, the fault prediction weighted channel will perform weighted processing on the two fault prediction coefficients according to a certain weight distribution strategy. The distribution of weights can be determined according to the actual needs and operation strategies of the power grid. For example, if the power grid operator pays more attention to the economic losses and social impacts that may be caused by the failure, then the weight of the fault risk prediction coefficient may be higher. Through weighted processing by the fault prediction weighted channel, a comprehensive main power grid fault prediction coefficient is generated, which can help operation and maintenance personnel to timely understand the operating status and potential risks of the power grid, so as to take corresponding preventive measures to avoid or reduce the impact of the failure on the power grid.

[0028] Step S300, determining whether the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold; if the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold, activating the first microgrid state switching strategy and the first microgrid control channel.

[0029] Preferably, the main grid fault prediction coefficient is compared with the main grid fault prediction threshold, wherein the main grid fault prediction threshold is a standard value set after comprehensive consideration of factors such as the actual situation of the grid, operation strategy and safety standards. When the main grid fault prediction coefficient is greater than or equal to the main grid fault prediction threshold, it means that the possibility of a fault in the current state of the grid is high, or the risk that may be caused by the fault is high. Then, the first strategy of microgrid state switching is activated to ensure the stable operation and power supply reliability of the grid. Specifically, the first strategy of microgrid state switching is actually a control instruction. In the grid-connected operation state, the microgrid is connected to the main grid and exchanges electric energy. When the main grid fails or is abnormal, the microgrid can quickly switch to the island operation mode through the first strategy of microgrid state switching, disconnect from the main grid, and continue to supply power relying on its own power generation equipment and energy storage device.

[0030] Preferably, in the island operation mode, a main power source is selected to provide a reference for voltage and frequency. This main power source is usually an energy storage device, a diesel generator or other renewable energy power generation equipment. By switching the control strategy, the main power source is operated in a constant voltage and frequency (V / f) control mode to ensure the stable operation of the microgrid. In addition to the main power source, there may be other distributed power sources in the microgrid. In the island operation mode, these distributed power sources need to be coordinated and controlled to provide a constant power output (P / Q control mode) to meet the load demand in the microgrid. Corresponding to the first microgrid state switching strategy is the first microgrid regulation channel, which is mainly used to switch the microgrid state. Real-time power regulation and optimization are carried out during the switching process. The first channel of microgrid regulation can monitor the key parameters of the power grid such as voltage, current, frequency, etc. in real time through sensors and monitoring equipment installed at each node of the microgrid, and analyze and process these data to understand the operating status and load demand of the power grid; at the same time, according to the real-time monitoring data and load demand prediction results, the power in the microgrid is distributed and dispatched, including adjusting the output of distributed power sources, optimizing the charging and discharging strategies of energy storage devices, etc.; the operating status of the microgrid is monitored in real time, faulty equipment or lines are discovered and isolated in time, and corresponding protection measures are taken to prevent the fault from expanding or affecting the normal operation of other equipment.

[0031] Step S400: According to the power supply priority evaluation rule, power supply priority analysis is performed on the load device set in the target area to establish a device power supply priority distribution.

[0032] Preferably, power supply priority analysis is performed on the load equipment set in the target area according to the power supply priority evaluation rule, wherein the power supply priority evaluation rule is the basis for determining the power supply order of the load equipment. The power supply priority evaluation rule is usually formulated based on multiple factors such as the importance of the equipment, power demand, and fault impact. For example, key equipment (such as medical equipment, emergency lighting, etc.) usually has a higher power supply priority to ensure continuous operation when power is insufficient. Specifically, power supply priority analysis is to classify the load equipment in the target area according to factors such as the purpose, power demand, and operating characteristics of the equipment, which helps to better understand the power demand and power supply priority of the equipment. Then, based on the equipment classification, each type of equipment is prioritized according to the power supply priority evaluation rules. For example, the power demand, failure impact, and operating stability of the equipment are comprehensively considered. According to the evaluation results, the equipment is prioritized, and the sorting results will reflect the power supply order of the equipment when power is insufficient. Then, the equipment power supply priority distribution is established, that is, the sorted equipment is listed in the priority distribution table according to the priority order, and the power supply priority of each type of equipment is clearly displayed. In the actual operation process, the power supply priority of the equipment may change due to various factors (such as equipment failure, changes in power demand, etc.). A dynamic adjustment mechanism is established to update and adjust the priority distribution table according to the actual situation to ensure that key equipment is given priority power supply when power is insufficient, thereby ensuring the stability and reliability of the power system.

[0033] Step S400 further includes step S410, wherein the power supply priority evaluation rule includes a power supply priority evaluation multidimensional index and a power supply priority evaluation multidimensional weight, wherein the power supply priority evaluation multidimensional index includes equipment criticality, equipment power failure loss and equipment power redundancy, and the power supply priority evaluation multidimensional weight includes a predetermined weight coefficient corresponding to each index in the power supply priority evaluation multidimensional index; step S420, basic information is collected according to the load equipment set to obtain each equipment information set; step S430, based on each equipment information set, the power supply priority of the load equipment set is evaluated according to the power supply priority evaluation multidimensional index to obtain each equipment evaluation coefficient set; step S440, normalization is performed according to the evaluation coefficient set of each equipment to obtain the evaluation results of each equipment; step S450, weighted calculation is performed on the evaluation results of each equipment based on the power supply priority evaluation multidimensional weight to obtain multiple equipment power supply priority evaluation coefficients; step S460, the multiple equipment power supply priority evaluation coefficients are arranged in descending order to generate the equipment power supply priority distribution.

[0034] Preferably, the power supply priority evaluation rule includes a power supply priority evaluation multidimensional index and a power supply priority evaluation multidimensional weight, that is, the power supply priority of the load equipment set is evaluated based on the multidimensional index and the weight. The power supply priority evaluation multidimensional index includes equipment criticality, equipment power failure loss and equipment power redundancy. Specifically, equipment criticality is to evaluate the importance of the equipment in the power system. Key equipment (such as medical equipment, data centers, etc.) usually has a higher power supply priority. Equipment power failure loss is to consider the economic loss or social impact that may be caused by equipment power failure. Equipment with high power failure loss (such as key machines on the production line, traffic lights, etc.) should be given priority in power supply when the power supply is insufficient. Equipment power redundancy is used to evaluate whether the equipment has a backup power supply or redundant power supply. Equipment with high power redundancy may have a relatively low power supply priority because they can cope with power outages to a certain extent. The multidimensional weight of the power supply priority evaluation refers to the weight corresponding to each multidimensional index of the power supply priority evaluation, reflecting the relative importance of different indicators in the evaluation process. Then, basic information of the load equipment set in the target area is collected, including key information such as the type, power, purpose, and operating status of the equipment.

[0035] Preferably, the power supply priority of the load equipment set is evaluated based on the collected information sets of each device and combined with the multi-dimensional indicators of power supply priority evaluation (equipment criticality, equipment power failure loss, and equipment power supply redundancy), that is, for each device, the corresponding evaluation coefficient is calculated according to its criticality, power failure loss, and power supply redundancy to obtain the evaluation coefficient set of each device. Since the evaluation coefficients of different devices may be at different orders of magnitude, the evaluation coefficient set of each device is normalized and sorted, and the evaluation coefficients of all devices are converted to the same order of magnitude for fair comparison and sorting. Then, based on the multi-dimensional weight of power supply priority evaluation (that is, the predetermined weight coefficient corresponding to each indicator), the evaluation results of each device are weighted calculated. The result of the weighted calculation is the power supply priority evaluation coefficient of each device, which reflects the relative position of the device in the power supply priority. Finally, the power supply priority evaluation coefficients of multiple devices are arranged in descending order to generate a power supply priority distribution of the device, which clearly shows the order of each device in the power supply priority, helping power dispatchers to make reasonable decisions when power is insufficient, giving priority to the power supply of key equipment and important loads, while helping to optimize the power supply layout and load distribution, and improving the overall efficiency and reliability of the power grid.

[0036] Step S500: deeply optimize the first strategy for microgrid state switching based on the device power supply priority distribution and the first microgrid control channel to obtain a first microgrid control solution.

[0037] Preferably, critical loads with the highest power supply priority in the power system are identified based on the equipment power supply priority distribution, such as medical equipment, data centers, emergency lighting, etc., and then the loads in the microgrid are reasonably distributed according to the equipment power supply priority to ensure that the critical loads can get sufficient power supply when the power is insufficient, that is, by adjusting the load distribution, the impact of power outages on critical loads is minimized, and then the real-time monitoring function in the first channel of microgrid regulation is used to continuously monitor the operating status of the microgrid, collect and analyze key parameters such as voltage, current, frequency, etc. of the microgrid, as well as load demand and energy supply. Then, based on the real-time monitoring data and analysis results, the microgrid regulation strategy is dynamically adjusted. For example, when the power is insufficient, the power supply of critical loads is prioritized, while reducing the power demand of non-critical loads, and the first strategy of microgrid state switching is deeply optimized. Specifically, the balance between the microgrid and the external power grid is optimized. A sliding switching strategy is used to ensure that key parameters such as voltage and frequency remain stable during the switching of the grid state, so as to achieve rapid and smooth switching of the microgrid from the grid-connected operation state to the island operation state. Within the microgrid, the coordinated control strategy between different power sources is optimized, that is, according to the power supply priority distribution of the equipment and the load demand, the output of the distributed power source and the charging and discharging strategy of the energy storage device are reasonably adjusted to improve the energy utilization efficiency. The fault detection function in the first channel of microgrid control is used to timely discover and isolate faulty equipment or lines. For example, according to the power supply priority distribution of the equipment, the power supply of key loads is restored first, and necessary protection measures are taken to prevent the expansion of the fault. The microgrid control strategy is comprehensively evaluated and optimized, considering multiple factors such as cost-effectiveness, system stability, and energy utilization rate, to determine the optimal control plan, that is, the first microgrid control plan, and the control plan is adjusted and optimized as necessary according to the actual situation to ensure the stable operation and efficient utilization of the microgrid.

[0038] Step S500 further includes step S510, obtaining the predicted fault operation and maintenance duration of the main power grid; step S520, predicting the power supply capacity of the microgrid according to the predicted fault operation and maintenance duration, and determining the predicted power supply capacity; step S530, based on the power supply priority distribution of the equipment, optimizing the load equipment set according to the predicted fault operation and maintenance duration and the predicted power supply capacity, and determining the key load equipment set; step S540, based on the predicted fault operation and maintenance duration and the key load equipment set, according to the first microgrid control channel, generating a first microgrid control decision; step S550, supplementing the first microgrid state switching strategy according to the first microgrid control decision, and generating the first microgrid control plan.

[0039] Preferably, the historical fault data of the power grid is analyzed, and the faults of the main power grid are predicted in combination with multiple factors such as the current power grid operating status, weather conditions, equipment aging, etc., and the time required for fault operation and maintenance is estimated. The accuracy of the fault operation and maintenance prediction time is crucial for the subsequent microgrid power supply capacity prediction and load equipment set optimization. Then, the power capacity that the microgrid can provide during the fault period is evaluated based on the fault operation and maintenance prediction time. That is, the power generation capacity of the distributed power sources in the microgrid, the storage energy of the energy storage device, and the changes in load demand and power supply losses are comprehensively considered to predict the maximum power supply capacity that the microgrid can provide during the main power grid fault, thereby determining the predicted power supply capacity.

[0040] Preferably, the load device set is optimized according to the device power supply priority distribution, which means that under limited power supply, those devices with higher power supply priority are given priority to be powered. By sorting and screening the load device set, the key load device set that needs priority power supply during the fault can be determined, and then the first microgrid control decision is generated according to the fault operation and maintenance prediction duration and the capacity of the first microgrid control channel, such as regulating the distributed power supply, energy storage device, and load in the microgrid to ensure that the key load device set can obtain sufficient power supply during the fault, wherein the control decision may include adjusting the output of the distributed power supply, optimizing the charging and discharging strategy of the energy storage device, and adjusting the power demand of the load, etc. Finally, according to the first microgrid control decision, the first microgrid state switching strategy is supplemented and improved to generate the final microgrid control first plan, including real-time monitoring and adjustment of the operating status of the microgrid to ensure that the microgrid can stably and reliably supply power to the key load device set during the fault, and consider the microgrid re-grid operation strategy after the fault is recovered to ensure the overall stability and reliability of the power system.

[0041] Step S530 further includes step S531, predicting the distribution capacity of the load device set according to the predicted duration of the fault operation and maintenance, and determining the predicted distribution capacity of each device; step S532, summing up the predicted distribution capacity of each device to obtain the total predicted distribution capacity; step S533, judging whether the total predicted distribution capacity is less than or equal to the predicted power supply capacity; step S534, if the total predicted distribution capacity is less than or equal to the predicted power supply capacity, adding the load device set to the key load device set; step S535, if the total predicted distribution capacity is greater than the predicted power supply capacity, calculating the difference between the total predicted distribution capacity and the predicted power supply capacity to determine the shortfall power supply capacity; step S536, based on the shortfall power supply capacity and the predicted distribution capacity of each device, eliminating the load device set according to the power supply priority distribution of the equipment to generate the key load device set.

[0042] Preferably, according to the predicted duration of the main grid fault operation and maintenance, the distribution capacity of each load device is predicted to determine the predicted distribution capacity of each device. Specifically, it usually involves consideration of factors such as the device power demand, operating time, and load mode that may change due to the fault, to ensure that each device can obtain sufficient power supply during the fault. Then, the predicted distribution capacity of each device is added and calculated to obtain the total predicted distribution capacity, which represents the total power supply expected to be obtained by the set of load devices during the fault. Then, the total predicted distribution capacity is compared with the predicted power supply capacity of the microgrid, wherein the predicted power supply capacity is determined based on factors such as distributed power sources, energy storage devices, and possible external support power sources in the microgrid, and represents the power supply of the microgrid during the fault. The maximum power supply that can be provided during a fault. If the total predicted distribution capacity does not exceed the predicted power supply capacity, it means that the microgrid has sufficient power supply to meet the needs of all load devices, and the current load device set is regarded as the critical load device set; if the total predicted distribution capacity exceeds the predicted power supply capacity, there is a power supply gap. The size of the gap is determined by calculating the difference between the two, that is, the gap power supply capacity. After determining the gap power supply capacity, the load device set is screened according to the power supply priority distribution of the equipment to determine which equipment can be powered during the fault. By eliminating those equipment with lower priority, a critical load device set that can be powered during the fault is generated to avoid overload and instability of power supply.

[0043] Step S540 further includes step S541, obtaining microgrid monitoring data of the microgrid according to the power sensing network; step S542, performing power supply demand analysis on the key load equipment set based on the fault operation and maintenance prediction duration to obtain the power supply demand distribution of key equipment; step S543, inputting the microgrid monitoring data, the fault operation and maintenance prediction duration and the power supply demand distribution of key equipment into the first channel of microgrid regulation to obtain the first decision of microgrid regulation.

[0044] Preferably, a power sensing network (which may include sensors, smart meters, remote monitoring systems and other equipment) is used to collect real-time operation data of the microgrid, including key parameters such as voltage, current, frequency, power factor, load demand, distributed power output, and energy storage device status. After the key load equipment set is determined, the power supply demand of these devices is analyzed according to the predicted duration of fault operation and maintenance. By considering factors such as the power demand, operating time, and load mode that may change due to the fault of each device, the power supply demand distribution of the key equipment is obtained, that is, the power supply required by each device in different time periods; finally, the microgrid monitoring data, The predicted duration of fault operation and maintenance and the distribution of power supply demand of key equipment are used as input information and transmitted to the first channel of microgrid control. The first channel of microgrid control processes and analyzes the input information in real time to formulate the optimal microgrid control strategy, that is, to obtain the first decision of microgrid control, such as adjusting the output of distributed power sources, optimizing the charging and discharging strategy of energy storage devices, adjusting the power demand of loads, and possible microgrid state switching strategies, etc., to ensure that during fault operation and maintenance, the microgrid can stably and reliably provide power supply to the key load equipment set, while minimizing the impact on other non-critical loads as much as possible to maintain the overall stability and reliability of the microgrid.

[0045] Step S600: regulating the microgrid based on the first microgrid regulation scheme.

[0046] Preferably, according to the determined first microgrid control scheme, the actual operation state of the microgrid is controlled and optimized. Specifically, according to the requirements of the first microgrid control scheme, the output of distributed power sources is adjusted, such as increasing or decreasing the power generation of renewable energy (such as solar energy and wind energy), or adjusting the output of other types of distributed power sources (such as small gas turbines, fuel cells, etc.) to meet the power demand of the microgrid; the charging and discharging strategy of the energy storage device is optimized, such as releasing the electric energy in the energy storage device to supplement the power supply when the power demand is peak, and charging to store electric energy when the power demand is low to meet the future peak demand. Make preparations; manage and adjust the load according to the first microgrid control plan, such as optimizing power consumption time and reducing unnecessary power consumption through smart meters and control systems; if the first microgrid control plan includes a state switching strategy, the state switching operation can be performed when necessary. For example, when the main power grid fails, the microgrid switches from grid-connected mode to island mode to ensure the power supply of key load equipment sets; during the entire control process, the operating status of the microgrid is monitored in real time. If any abnormal situation or deviation from the expected situation is found, it is necessary to make timely adjustments and optimizations to ensure the stability and reliability of the microgrid.

[0047] Step S600 further includes step S610, when the main grid returns to normal, activating the second microgrid state switching strategy and the second microgrid control channel; step S620, deeply optimizing the second microgrid state switching strategy according to the second microgrid control channel to obtain a second microgrid control scheme; step S630, regulating the microgrid based on the second microgrid control scheme.

[0048] Preferably, when the main grid resumes normal power supply capacity, the microgrid needs to switch from the current operating state (island mode) back to the grid-connected mode of the main grid, that is, activate the second microgrid state switching strategy, which describes in detail how to smoothly transition from the current state to the grid-connected state, including necessary steps, condition judgments, time windows, etc. Corresponding to the state switching strategy, the second microgrid control channel is used to handle microgrid control in the grid-connected operation mode to adapt to the special needs and constraints of grid-connected operation; after activating the state switching strategy and control channel, these two elements need to be deeply optimized, including considering various factors in the grid-connected process, such as voltage fluctuations, frequency stability, and power balance. Etc., as well as the interaction and influence between the microgrid and the main grid. Through optimization, the state switching process can be ensured to be smooth and fast, and the impact on the microgrid and the main grid can be minimized. Finally, a specific second microgrid control plan is formed, which describes in detail the control strategy, control parameters, adjustment steps, etc. of the microgrid under the grid-connected operation mode to ensure that the microgrid can be stably and efficiently connected to the main grid for operation, and according to the obtained second microgrid control plan, actual control operations are performed on the microgrid, such as adjusting the output of distributed power sources, optimizing the charging and discharging strategies of energy storage devices, adjusting the power demand of loads, etc., to ensure that the microgrid can maintain a stable, reliable and efficient operating state under the grid-connected operation mode.

[0049] Step S620 further includes step S621, when the main grid returns to normal, obtaining the main grid update monitoring results and the microgrid update monitoring results according to the power sensing network; step S622, performing power supply demand analysis according to the target area to obtain the regional power supply demand; step S523, inputting the main grid update monitoring results, the microgrid update monitoring results and the regional power supply demand into the second channel of microgrid regulation to obtain a second microgrid regulation decision; step S624, supplementing the second microgrid state switching strategy according to the second microgrid regulation decision to generate the second microgrid regulation plan.

[0050] Preferably, when the main power grid returns to normal, the updated monitoring results of the main power grid and the microgrid can be obtained in real time through the power sensing network, including key parameters such as the voltage, current, frequency, power factor of the main power grid and the distributed power output, energy storage device status, load demand, etc. in the microgrid. According to the economic development, population distribution, industrial structure and other factors of the target area, power supply demand analysis is carried out to obtain key information such as the total power demand, peak power consumption period, power consumption characteristics, etc. in the area, which helps to ensure that the microgrid can meet the power demand of the target area. Then, the main power grid update monitoring results, microgrid update monitoring results and regional power supply demand are updated. The key information such as search is input into the second channel of microgrid control. The second channel of microgrid control performs real-time analysis and calculation on the input data to generate the second decision of microgrid control. Then, according to the second decision of microgrid control, the second strategy of microgrid state switching is supplemented and improved, including determining the best time, steps and conditions for switching the microgrid from island mode to grid-connected mode. Finally, based on the actual situation of the main grid and microgrid and the regional power supply demand, the second microgrid control plan is generated, which describes in detail the control strategy, control parameters, adjustment steps, etc. of the microgrid under the grid-connected operation mode to ensure that the microgrid can be stably and efficiently connected to the main grid for operation.

[0051] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A microgrid intelligent control method, characterized in that: The method comprises: Real-time monitoring of the main power grid in the target area is performed according to the power sensing network to obtain main power grid monitoring data, wherein the microgrid in the target area is in a grid-connected operation state; Inputting the main power grid monitoring data into the main power grid fault prediction integrated channel to obtain the main power grid fault prediction coefficient, including: The main power grid fault prediction integrated channel includes a main power grid fault probability prediction channel, a main power grid fault risk prediction channel, a fault prediction normalization processing channel and a fault prediction weighted channel; Inputting the main power grid monitoring data into the main power grid fault probability prediction channel to obtain a main power grid fault probability prediction coefficient; Based on the main power grid monitoring data and according to the main power grid fault risk prediction channel, a main power grid fault risk prediction coefficient is obtained; Inputting the main power grid fault probability prediction coefficient and the main power grid fault risk prediction coefficient into the fault prediction normalization processing channel to obtain a fault prediction normalization processing result; Inputting the fault prediction normalization processing result into the fault prediction weighted channel to generate the main power grid fault prediction coefficient; Determine whether the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold, and if the main grid fault prediction coefficient is greater than / equal to the main grid fault prediction threshold, activate the first microgrid state switching strategy and the first microgrid control channel; According to the power supply priority evaluation rule, power supply priority analysis is performed on the load device set in the target area to establish a device power supply priority distribution; Based on the equipment power supply priority distribution and the first microgrid control channel, the first microgrid state switching strategy is deeply optimized to obtain a first microgrid control solution; The microgrid is regulated based on the first microgrid regulation scheme.

2. A microgrid intelligent control method according to claim 1, characterized in that: According to the power supply priority evaluation rule, the power supply priority of the load equipment set in the target area is parsed to establish the equipment power supply priority distribution, including: The power supply priority evaluation rule includes a power supply priority evaluation multidimensional index and a power supply priority evaluation multidimensional weight, wherein the power supply priority evaluation multidimensional index includes equipment criticality, equipment power failure loss and equipment power redundancy, and the power supply priority evaluation multidimensional weight includes a predetermined weight coefficient corresponding to each index in the power supply priority evaluation multidimensional index; Collect basic information according to the load device set to obtain information sets of each device; Based on the information sets of each device, the power supply priority of the load device set is evaluated according to the power supply priority evaluation multi-dimensional index to obtain an evaluation coefficient set of each device; Normalizing and arranging the evaluation coefficient sets of each device to obtain evaluation and arrangement results of each device; Performing weighted calculation on the evaluation results of each device based on the multi-dimensional weight of the power supply priority evaluation to obtain power supply priority evaluation coefficients of multiple devices; The multiple device power supply priority evaluation coefficients are arranged in descending order to generate the device power supply priority distribution.

3. A microgrid intelligent control method according to claim 1, characterized in that: The first strategy for switching the microgrid state is deeply optimized based on the device power supply priority distribution and the first microgrid control channel to obtain a first microgrid control solution, including: Obtaining a predicted duration of fault operation and maintenance of the main power grid; Predicting the power supply capacity of the microgrid according to the predicted duration of the fault operation and maintenance, and determining the predicted power supply capacity; Based on the equipment power supply priority distribution, the load equipment set is optimized according to the fault operation and maintenance prediction duration and the predicted power supply capacity to determine the key load equipment set; Based on the predicted fault operation and maintenance duration and the key load device set, and according to the first microgrid control channel, a first microgrid control decision is generated; The first microgrid state switching strategy is supplemented according to the first microgrid control decision to generate the first microgrid control plan.

4. A microgrid intelligent control method as claimed in claim 3, characterized in that: Based on the equipment power supply priority distribution, the load equipment set is optimized according to the fault operation and maintenance prediction duration and the predicted power supply capacity to determine the key load equipment set, including: According to the predicted duration of the fault operation and maintenance, the power distribution capacity of the load device set is predicted to determine the predicted power distribution capacity of each device; Performing sum calculation based on the predicted power distribution capacity of each device to obtain the total predicted power distribution capacity; Determining whether the total predicted power distribution capacity is less than or equal to the predicted power supply capacity; If the total predicted power distribution capacity is less than or equal to the predicted power supply capacity, adding the load device set to the critical load device set; If the total predicted power distribution capacity is greater than the predicted power supply capacity, the difference between the total predicted power distribution capacity and the predicted power supply capacity is calculated to determine the shortfall power supply capacity; Based on the shortfall power supply capacity and the predicted power distribution capacity of each device, the load device set is eliminated according to the device power supply priority distribution to generate the critical load device set.

5. A microgrid intelligent control method as claimed in claim 3, characterized in that: Based on the predicted fault operation and maintenance duration and the key load device set, and according to the first microgrid control channel, a first microgrid control decision is generated, including: According to the power sensing network, obtaining microgrid monitoring data of the microgrid; Performing power supply demand analysis on the key load equipment set based on the predicted fault operation and maintenance duration to obtain power supply demand distribution of key equipment; The microgrid monitoring data, the fault operation and maintenance prediction duration and the key equipment power supply demand distribution are input into the first microgrid control channel to obtain the first microgrid control decision.

6. A microgrid intelligent control method according to claim 1, characterized in that: The microgrid is regulated based on the first microgrid regulation scheme, including: When the main grid returns to normal, activating the second strategy for microgrid state switching and the second channel for microgrid regulation; Deeply optimize the second strategy for switching the microgrid state according to the second microgrid control channel to obtain a second microgrid control solution; The microgrid is regulated based on the second microgrid regulation scheme.

7. A microgrid intelligent control method according to claim 6, characterized in that: The second strategy for switching the microgrid state is deeply optimized according to the second microgrid control channel to obtain a second microgrid control solution, including: When the main grid returns to normal, obtaining the main grid update monitoring result and the microgrid update monitoring result according to the power sensing network; Performing power supply demand analysis according to the target area to obtain regional power supply demand; Inputting the main grid update monitoring result, the microgrid update monitoring result and the regional power supply demand into the microgrid control second channel to obtain a microgrid control second decision; The second strategy for switching the state of the microgrid is supplemented according to the second decision for microgrid regulation to generate the second microgrid regulation scheme.

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