A reliability assessment method and system for multi-microgrid systems considering random correlation

Abstract

The present invention discloses a multi-microgrid system reliability assessment method and system that considers random correlation. The method includes the following steps: obtaining a number of operating variables of a microgrid group in a preset area, performing random correlation analysis on the several operating variables, and constructing an output model for the microgrid group based on the analysis results, the output model including a photovoltaic output model and a wind power output model; obtaining a load demand forecast value for a preset future time based on a load forecast model for the preset area; generating a scheduling strategy for the microgrid group based on the load demand forecast value and the output model; configuring power supply for the preset area according to the scheduling strategy and obtaining real-time power consumption data, and calculating the reliability of the microgrid group's scheduling strategy based on the real-time power consumption data. Taking into account the differences in power supply reliability requirements of loads within the microgrid group, the method fully taps the power supply potential of the microgrid group by modeling the energy allocation strategy within the microgrid group, laying the foundation for formulating corresponding operating strategies and optimizing decisions.

Description

Technical Field

[0001] The present invention relates to the field of microgrid control technology, and in particular to a multi-microgrid system reliability assessment method and system considering random correlation. Background Art

[0002] Environmental pollution caused by energy consumption is becoming more and more serious. The use of new energy for power generation is the future development trend. The combined heating, cooling and power systems proposed in recent years have become a research hotspot due to their advantages of improving energy utilization, reducing environmental pollution and renewable resources.

[0003] With the development of microgrid technology, numerous microgrid projects have been built across various regions. In industrial parks and other settings, microgrid clusters, consisting of several microgrids, have become a trend in microgrid development. A microgrid cluster is a more complex system composed of several microgrids, and it has distinct characteristics compared to a microgrid. Within a microgrid cluster, there is energy interoperability and support between the microgrids.

[0004] However, the power supply reliability of a microgrid cluster is more complex than that of a single microgrid. Current microgrid research primarily focuses on single microgrids, without considering the simultaneous connection of multiple microgrids to the distribution network. This interconnected regional multi-microgrid system, composed of geographically adjacent microgrids, can affect microgrid operation. Therefore, the operational characteristics of multiple microgrids in this scenario differ from those of a single microgrid. The distributed generation configurations and load demands of individual microgrids within a region are not uniform. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a multi-microgrid system reliability assessment method and system that considers random correlation. Based on the differences in power supply reliability requirements of loads within a microgrid group, the energy allocation strategy within the microgrid group is modeled, thereby fully tapping the power supply potential of the microgrid group and laying the foundation for formulating corresponding operation strategies and optimizing decisions.

[0006] To solve the above technical problems, a first aspect of an embodiment of the present invention provides a multi-microgrid system reliability assessment method considering random correlation, comprising the following steps:

[0007] Acquire a plurality of operating variables of a microgrid group in a preset area, perform random correlation analysis on the plurality of operating variables, and construct an output model of the microgrid group based on the analysis results, the output model including: a photovoltaic output model and a wind power output model;

[0008] Obtaining a load demand forecast value for a preset time in the future based on a load forecast model for the preset area;

[0009] generating a scheduling strategy for the microgrid group based on the load demand forecast value and the output model;

[0010] Power supply configuration is performed in the preset area according to the scheduling strategy and real-time power consumption data is obtained, and the reliability of the scheduling strategy of the microgrid group is calculated based on the real-time power consumption data.

[0011] Furthermore, the operating variables include: wind power output data O w And solar energy output data l ;

[0012] The wind energy output data O w Expressed as in, The geographical location is d w (x w ,y w )’s wind energy output value of the wind power generation device;

[0013] The light energy output data O l Expressed as in, The geographical location is d l (x l ,y l ) of the photovoltaic power generation device.

[0014] Furthermore, the performing random correlation analysis on the plurality of operating variables includes:

[0015] The correlation coefficient ρ is used to characterize the correlation between the wind energy output data and the solar energy output data:

[0016]

[0017] Wherein, i is the wind energy output data O w The number of groups, j is the light energy output data O l The number of groups, ρ ij is the insignificant correlation coefficient between the wind power output data and the solar power output data, R i and R j are the indices of the sample matrices of the wind power output data and the solar power output data, Cov(·) represents the covariance, and δ(·) represents the standard deviation;

[0018] When ρ=0, it means that there is no monotonic relationship between the wind power generation devices and photovoltaic power generation devices in the preset area;

[0019] When ρ>ρ 0 When , it indicates that the wind power generation device and the photovoltaic power generation device in the preset area are positively correlated with each other;

[0020] When ρ<ρ 0When , it means that the wind power generation device and the photovoltaic power generation device in the preset area are negatively correlated with each other, ρ 0 is the correlation threshold.

[0021] Furthermore, constructing the output model of the microgrid group based on the analysis results includes:

[0022] Construct geographic location d l (x l ,y l ) is expressed as follows:

[0023]

[0024] Among them, G is the light intensity, A is the photovoltaic cell area, is the nominal efficiency, T C is the temperature of the photovoltaic cell, ρ is the temperature coefficient;

[0025] Construct geographic location d w (x w ,y w ) wind power generation model:

[0026]

[0027] Among them, P r is the rated power of the wind turbine, v is the current wind speed, v in is the starting wind speed, v out is the shutdown wind speed, v r is the rated wind speed;

[0028] The output model of the microgrid group is expressed as follows:

[0029]

[0030] Where a and b represent the number of photovoltaic power generation devices and wind power generation devices that meet the constraint condition, respectively. The constraint condition is ρ>ρ 0 , P is the photoelectric effective mixing power.

[0031] Furthermore, before obtaining the load demand forecast value for a preset time in the future based on the load forecast model of the preset area, the method further includes:

[0032] The historical load demand data of the preset area is divided according to the preset time period, which is expressed as X={x 1 ,x 2 ,…,x T}, T is the number of the historical time period;

[0033] Create a mapping function f: X→Y; Y is the load demand, expressed as Y={y 1 ,y 2 ,…,y T};

[0034] The mapping function is a time mapping and a weather status mapping of a fixed time period within a preset time period of the preset area.

[0035] Furthermore, generating a dispatching strategy for the microgrid group based on the load demand forecast value and the output model includes:

[0036] Based on the output model, calculating the photovoltaic effective hybrid power of each microgrid in the microgrid group in the current time period, and selecting at least one microgrid that meets the load demand forecast value;

[0037] In combination with the constraint conditions, the selected at least one microgrid is effectively scheduled.

[0038] Furthermore, the real-time electricity consumption data includes: the total electricity consumption time of users in the preset area, the number of power outages for users, the total power outage time for users, and the cause of the power outage.

[0039] Furthermore, the calculation formula for the reliability of the scheduling strategy of the microgrid group is as follows:

[0040]

[0041] Where f1 is the average number of power outages for power users, λ c is the number of power outages, N c is the user number, ∑N c is the total number of users, ∑λ c N c is the total number of power outages for users, f2 is the average power outage duration for users, u c is the duration of a single power outage, ∑u c N c The total duration of power outages for users.

[0042] Furthermore, after calculating the dispatching strategy reliability of the microgrid group based on the real-time power consumption data, the method further includes:

[0043] With the purpose of reducing the average value f1 of the number of power outages for power users and the average value f2 of the power outage duration for power users, the output model of the microgrid group is optimized.

[0044] Accordingly, a second aspect of an embodiment of the present invention provides a multi-microgrid system reliability assessment system considering random correlation, comprising:

[0045] a model building module, configured to obtain a plurality of operating variables of a microgrid group in a preset area, perform random correlation analysis on the plurality of operating variables, and build an output model of the microgrid group based on the analysis results, the output model including a photovoltaic output model and a wind power output model;

[0046] A load forecasting module, configured to obtain a load demand forecast value for a preset time in the future based on a load forecasting model for the preset area;

[0047] A strategy generation module, configured to generate a scheduling strategy for the microgrid group based on the load demand forecast value and the output model;

[0048] A reliability calculation module is used to configure the power supply of the preset area according to the scheduling strategy and obtain real-time power consumption data, and calculate the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data.

[0049] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned multi-microgrid system reliability assessment method considering random correlation.

[0050] Accordingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned multi-microgrid system reliability assessment method considering random correlation.

[0051] The above technical solutions of the embodiments of the present invention have the following beneficial technical effects:

[0052] 1. A correlation analysis was established for photovoltaic power generation devices and wind power generation devices at different geographical locations within each microgrid within the microgrid cluster. The mutual relationship was analyzed from the dimensions of location relationship and power generation type, providing a basis for subsequent scheduling;

[0053] 2. A hybrid processing model for microgrid clusters was established based on correlation analysis, achieving high-intensity complementarity between photovoltaic and wind energy, fully utilizing resources and improving power supply efficiency;

[0054] 3. Combined with the dispatching strategy of regional multi-microgrid system, it is expected to provide guidance for high-reliability power supply of microgrid clusters, which not only provides guidance for the coordinated operation and dispatching of microgrid clusters, but also ensures high-reliability power supply of microgrid clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1This is a flow chart of a multi-microgrid system reliability assessment method considering random correlation provided by an embodiment of the present invention;

[0056] Figure 2 This is a module block diagram of a multi-microgrid system reliability assessment system considering random correlation provided by an embodiment of the present invention.

[0057] Reference numerals:

[0058] 1. Model building module, 2. Load forecasting module, 3. Strategy generation module, 4. Reliability calculation module. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0060] Please refer to Figure 1 A first aspect of an embodiment of the present invention provides a multi-microgrid system reliability assessment method considering random correlation, comprising the following steps:

[0061] Step S100 , obtaining several operating variables of a microgrid group in a preset area, performing random correlation analysis on the several operating variables, and constructing an output model of the microgrid group based on the analysis results. The output model includes: a photovoltaic output model and a wind power output model.

[0062] Microgrid clusters are divided based on their geographic locations. Considering the internal complexity of each microgrid, which may include both photovoltaic and wind power generation, variables are pre-collected based on the internal patterns of the microgrid clusters. Correlation analysis is then performed to analyze the interrelationships between different types of power generation. The internal output patterns of the microgrid clusters are also analyzed based on these internal patterns.

[0063] Step S200: obtaining a load demand forecast value for a preset time in the future based on a load forecast model of a preset area.

[0064] Step S300: generating a dispatching strategy for the microgrid group based on the load demand forecast value and the output model.

[0065] According to the actual needs of users and combined with the internal mode of the microgrid group, an appropriate scheduling strategy is selected. This scheduling strategy can be used to directly schedule power generation devices within different microgrid groups and realize direct span scheduling between microgrid groups.

[0066] Step S400 , configuring power supply in a preset area according to a scheduling strategy and obtaining real-time power consumption data, and calculating the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data.

[0067] The power supply configuration is completed according to the scheduling strategy and real-time power consumption data during the power supply period is collected in real time. The reliability of the scheduling strategy is calculated based on the real-time power consumption data, and the output model is continuously optimized to increase the reliability of span scheduling.

[0068] The above-mentioned multi-microgrid system reliability assessment method considering random correlation constructs an output model by analyzing the random correlation of the operating variables of the microgrid group, which can more accurately reflect the actual output of energy sources such as photovoltaic and wind energy, and provide a more reliable basis for subsequent reliability assessment; based on the load forecast model, the load demand forecast value is obtained, and the scheduling strategy is generated in combination with the output model, which realizes the reasonable power supply configuration planning of the microgrid group and improves energy utilization efficiency; according to the scheduling strategy, power supply configuration is carried out and real-time power consumption data is obtained to calculate the reliability of the scheduling strategy, which can evaluate the reliability level of the multi-microgrid system in real time, discover potential problems in time and take corresponding measures, and ensure the stable operation of the power supply system.

[0069] Specifically, the operating variables in the preset regional microgrid group in step S100 include: wind power output data O w And solar energy output data l .

[0070] Wind power output data w Expressed as in, The geographical location is d w (x w ,y w ) of the wind power generation device; the light energy output data O l Expressed as in, The geographical location is d l (x l ,y l ) of the photovoltaic power generation device.

[0071] The operating variables in the preset regional microgrid group, namely the wind power output data O w And solar energy output data l Plays a vital role. Wind power output data wIt reflects the actual output capacity of the wind power generation part in the microgrid group. By analyzing its random correlation, we can better understand the uncertainty and volatility of wind power generation, so as to more accurately simulate the actual contribution of wind energy when building the output model. This helps to reasonably consider the availability of wind power generation in the formulation of scheduling strategies and improve the efficiency of the system's use of wind energy resources. l It reflects the output of photovoltaic power generation. Similarly, analyzing it can help us understand the characteristics of photovoltaic power generation, such as the changing patterns affected by factors such as weather and time. In the output model, the accurate inclusion of light output data can make the model closer to the actual situation and provide a more accurate prediction and planning basis for the reliable operation of the microgrid group.

[0072] In addition, the acquisition of geographic location is mainly to satisfy economic constraints during later scheduling. As for the economic constraints in this embodiment, existing technologies can be used for constraints, which will not be elaborated here.

[0073] The acquisition of the above data is not limited to a single microgrid group, but can be directly obtained and centrally analyzed in all microgrid groups, directly linking the power generation devices within the microgrid group.

[0074] Furthermore, the random correlation analysis of several operating variables in step S100 includes:

[0075] Step S110: using the rank correlation coefficient ρ to characterize the correlation between the wind power output data and the solar power output data:

[0076]

[0077] Among them, i is the wind power output data O w The number of groups, j is the light output data O l The number of groups, ρ ij is the innocuous correlation coefficient between wind power output data and solar power output data, R i and R j are the indices of the sample matrices of wind power output data and solar power output data, Cov(·) represents the covariance, and δ(·) represents the standard deviation.

[0078] By using the algebraic correlation coefficient ρ to characterize the wind power output data O w The correlation between the solar energy output data O and the solar energy output data O provides a quantitative basis for accurately constructing the output model of the microgrid group.

[0079] Specifically, the calculation of the nodal correlation coefficient takes into account the nodal (R i and R j), covariance (Cov(·)), and standard deviation (δ(·)). This calculation method can comprehensively reflect the degree of association between two sets of data, rather than being limited to the traditional linear correlation.

[0080] Accurately understanding the correlation between wind and solar output data is crucial for assessing the reliability of multi-microgrid systems. On the one hand, it helps to more fully understand the complementarity or substitutability between different energy sources, allowing for more rational consideration of the synergistic effects of multiple energy sources when constructing output models. For example, if wind and solar output data show a positive correlation, then in some cases, it can be expected that when wind output is high, solar output is also likely to be relatively high. This can provide a reference for developing scheduling strategies to fully leverage the advantages of multiple energy sources and improve system reliability and stability. On the other hand, quantifying correlations can also identify potential risk factors. If the correlation is too high, it may indicate that the system is overly dependent on a single energy source. If a problem with that energy source occurs, it could have a significant impact on the operation of the entire microgrid cluster.

[0081] When ρ = 0, it means that there is no monotonic relationship between the wind power generation devices and photovoltaic power generation devices in the preset area;

[0082] When ρ>ρ 0 When , it means that the wind power generation devices and photovoltaic power generation devices in the preset area are positively correlated with each other;

[0083] When ρ<ρ 0 When , it means that the wind power generation devices and photovoltaic power generation devices in the preset area are negatively correlated with each other, ρ 0 is the correlation threshold.

[0084] When ρ=0, it clearly indicates that there is no monotonic relationship between the two, which provides a clear basis for judgment, that is, in this case, there is no need to consider the synergistic or offsetting effect between the two. 0 and ρ<ρ 0 When the relationship is positive or negative, the positive and negative correlations are determined, which helps to take appropriate measures according to different relationship types when formulating scheduling strategies and reliability assessments.

[0085] For the case of positive correlation (ρ>ρ 0 ), which means that there is a certain degree of synchronization in the output of wind power generation devices and photovoltaic power generation devices. In the scheduling strategy, the possibility of using these two energy sources at the same time can be considered to improve the stability and reliability of the system. For example, when the wind is strong and the sun is sufficient, the use of these two energy sources can be increased to meet high load demand. For the case of negative correlation (ρ<ρ 0 ), when one energy source is insufficient, it can be supplemented by another energy source, thereby improving the system's ability to resist risks.

[0086] Introducing the correlation threshold ρ 0 , providing a clear boundary for determining positive and negative correlations. In practical applications, this threshold can be reasonably determined based on the specific conditions and historical data of a pre-defined region. By analyzing and statistically analyzing large amounts of data, a threshold suitable for that region can be found, improving the accuracy and reliability of judgments.

[0087] It should be noted that the correlation threshold ρ in this embodiment is 0 It is a dynamic parameter, which is continuously optimized through the subsequent reliability result analysis.

[0088] Furthermore, the step S100 of constructing the output model of the microgrid based on the analysis results includes:

[0089] Step S121, constructing a geographical location d l (x l ,y l ) is expressed as follows:

[0090]

[0091] Among them, G is the light intensity, A is the photovoltaic cell area, is the nominal efficiency, T C is the temperature of the photovoltaic cell, and ρ is the temperature coefficient.

[0092] Step S122: constructing a geographical location d w (x w ,y w ) wind power generation model:

[0093]

[0094] Among them, P r is the rated power of the wind turbine, v is the current wind speed, v in is the starting wind speed, v out is the shutdown wind speed, v r is the rated wind speed.

[0095] From the above content, we can know that the output model of the microgrid group is expressed as follows:

[0096]

[0097] Where a and b represent the number of photovoltaic power generation devices and wind power generation devices that meet the constraint conditions, respectively, and the constraint condition is ρ>ρ 0 , P is the photoelectric effective mixing power.

[0098] In order to meet the actual needs of users and make better scheduling decisions, the load forecasting model creation process in the embodiment of the present invention is as follows:

[0099] Furthermore, before obtaining the load demand forecast value for a preset time in the future based on the load forecast model of the preset area in step S200, the following steps may be further included:

[0100] Step S201: divide the historical load demand data of the preset area into preset time periods, expressed as X={x 1 ,x 2 ,…,x T}, T is the number of the historical time period.

[0101] This division method facilitates the systematic organization and analysis of historical data, making the load demand data of different time periods more clearly identifiable. By clearly numbering the historical time periods, it provides an orderly foundation for subsequent data analysis and model construction.

[0102] Step S202: Create a mapping function f: X→Y; Y is the load demand, expressed as Y={y 1 ,y 2 ,…,y T}.

[0103] The mapping function is a time mapping and a weather status mapping of a fixed time period within a preset time period of a preset area.

[0104] This mapping relationship takes into account two key factors: time and weather conditions. Time mapping reflects the changing patterns of load demand across different time periods, such as between weekdays and weekends, daytime and nighttime. Weather mapping accounts for the impact of weather on load demand, such as the increase in air conditioning load in hot weather and the increase in heating load in cold weather. By comprehensively considering both time and weather mapping, load demand forecasts for pre-set future times can be more accurately predicted, providing a more reliable basis for developing scheduling strategies for multi-microgrid systems.

[0105] For example, the above time periods can be divided by month or season. For example, if the load increases significantly in winter and summer, the time periods can be divided by season based on user demand. For example, the rainy season in summer causes a decrease in solar power generation; therefore, from the perspective of power supply, the time periods can be divided by month or day. The mapping relationship in this embodiment is relatively flexible and takes into account the status of both the electricity user and the power supplier in different time periods.

[0106] Therefore, the photovoltaic mixed power is different in different time periods, especially when it is affected by weather. For example, in rainy season and dry and windless weather. Therefore, according to the time period k mentioned above, the output model is used to calculate the photovoltaic effective mixed power in the microgrid during the same period, and the load demand y is selected to meet the forecast. k At least one microgrid, where y k ∈[y 1 ,y T ],k∈[1,T].

[0107] Furthermore, in step S300, the dispatching strategy of the microgrid group is generated based on the load demand forecast value and the output model, including:

[0108] Step S310 , based on the output model, calculate the photovoltaic effective hybrid power of each microgrid in the microgrid group in the current time period, and select at least one microgrid that meets the load demand prediction value.

[0109] This process can accurately determine which microgrids can contribute to meeting load demand based on actual power generation capacity and load demand; by calculating the effective photovoltaic hybrid power, it fully considers the comprehensive output of photovoltaic power generation and wind power generation, making the selected microgrid more targeted and effective.

[0110] Step S320: Effectively schedule the at least one selected microgrid in combination with the constraint conditions.

[0111] Combined with the constraint condition ρ>ρ 0 , effectively schedule the selected microgrid. Combined with the above, the correlation threshold ρ in this embodiment 0 It is a dynamic parameter, which is continuously optimized through the subsequent reliability result analysis.

[0112] At the same time, in order to verify the correlation threshold ρ 0 To ensure effective scheduling, embodiments of the present invention also include reliability verification through user experience. Specifically, after completing power supply configuration according to the scheduling strategy, real-time power usage data is collected during the power supply period. The real-time power usage data includes: the total power usage time of users in the preset area, the number of user power outages, the total power outage duration of users, and the cause of the power outage.

[0113] Furthermore, the calculation formula for the reliability of the microgrid scheduling strategy is as follows:

[0114]

[0115] Where f1 is the average number of power outages for power users, λ c is the number of power outages, N c is the user number, ∑N c is the total number of users, ∑λc N c is the total number of power outages for users, f2 is the average power outage duration for users, u c is the duration of a single power outage, ∑u c N c The total duration of power outages for users.

[0116] f1 represents the average number of power outages per user, and f2 represents the average duration of power outages per user. These two indicators provide a direct reflection of the stability and reliability of the microgrid's power supply to users under the microgrid's scheduling strategy. By counting and calculating the number and duration of power outages, we can clearly understand the microgrid's operational performance and provide concrete data support for evaluating the effectiveness of scheduling strategies.

[0117] The inclusion of parameters such as the number of outages, user ID, and duration of a single outage fully accounts for the actual conditions of different users, avoiding an overall assessment that ignores individual differences, making the reliability assessment more comprehensive and accurate. The number and duration of outages may vary for different users. By summing the statistics for each user and dividing it by the total number of users, we can obtain an average indicator reflecting the power supply reliability of the entire microgrid.

[0118] The reliability index derived from the above formula can help identify shortcomings in the current dispatching strategy, allowing for targeted optimization and improvement. For example, if the calculated average number of power outages is high, this indicates that the dispatching strategy needs to improve its ability to ensure power supply stability. Further analysis can be conducted to determine whether issues such as insufficient energy supply or equipment failure are to blame, and appropriate measures can be implemented, such as increasing backup power sources and optimizing equipment maintenance, to improve the reliability of the microgrid.

[0119] Furthermore, after calculating the reliability of the microgrid scheduling strategy based on the real-time power consumption data in step S400, the method further includes:

[0120] In step S410 , the output model of the microgrid is optimized for the purpose of reducing the average value f1 of the number of power outages for power users and the average value f2 of the power outage duration for power users.

[0121] By optimizing the output model of microgrid clusters, the reliability of multi-microgrid systems can be further improved. With the continuous accumulation and analysis of real-time power consumption data, problems in the output model can be promptly identified, allowing targeted adjustments and improvements to be made. This continuous optimization process enables the system to continuously adapt to changing power demands and operating environments, improving its adaptability and stability. Furthermore, reducing the number and duration of power outages can significantly improve user satisfaction. A reliable power supply is essential for production and life in modern society. By optimizing the output model to achieve this goal, users can be provided with more stable and reliable power services, enhancing their trust and reliance on the multi-microgrid system.

[0122] Accordingly, please refer to Figure 2 A second aspect of an embodiment of the present invention provides a multi-microgrid system reliability assessment system considering random correlation, comprising:

[0123] Model building module 1, which is used to obtain several operating variables of the microgrid group in a preset area, perform random correlation analysis on the several operating variables, and build an output model of the microgrid group based on the analysis results. The output model includes: a photovoltaic output model and a wind power output model;

[0124] Load forecasting module 2, which is used to obtain the load demand forecast value at a preset time in the future based on the load forecast model of the preset area;

[0125] Strategy generation module 3, which is used to generate a dispatching strategy for the microgrid group based on the load demand forecast value and the output model;

[0126] The reliability calculation module 4 is used to configure the power supply of the preset area according to the scheduling strategy and obtain real-time power consumption data, and calculate the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data.

[0127] Each functional module in the multi-microgrid system reliability assessment system considering random correlation can further refine different functional units according to the detailed process of the method steps to realize the functions corresponding to the method refinement process.

[0128] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned multi-microgrid system reliability assessment method considering random correlation.

[0129] Accordingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned multi-microgrid system reliability assessment method considering random correlation.

[0130] The embodiment of the present invention aims to protect a multi-microgrid system reliability assessment method and system considering random correlation, wherein the method includes the following steps: obtaining a number of operating variables of a microgrid group in a preset area, performing random correlation analysis on the several operating variables, and constructing an output model of the microgrid group based on the analysis results, the output model including: a photovoltaic output model and a wind power output model; obtaining a load demand forecast value for a preset time in the future based on a load forecast model of the preset area; generating a scheduling strategy for the microgrid group based on the load demand forecast value and the output model; configuring the power supply of the preset area according to the scheduling strategy and obtaining real-time power consumption data, and calculating the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data. The above technical solution has the following effects:

[0131] 1. A correlation analysis was established for photovoltaic power generation devices and wind power generation devices at different geographical locations within each microgrid within the microgrid cluster. The mutual relationship was analyzed from the dimensions of location relationship and power generation type, providing a basis for subsequent scheduling;

[0132] 2. A hybrid processing model for microgrid clusters was established based on correlation analysis, achieving high-intensity complementarity between photovoltaic and wind energy, fully utilizing resources and improving power supply efficiency;

[0133] 3. Combined with the dispatching strategy of regional multi-microgrid system, it is expected to provide guidance for high-reliability power supply of microgrid clusters, which not only provides guidance for the coordinated operation and dispatching of microgrid clusters, but also ensures high-reliability power supply of microgrid clusters.

[0134] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0135] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A multi-microgrid system reliability assessment method considering random correlation, characterized in that: The steps include: Acquire a plurality of operating variables of a microgrid group in a preset area, perform random correlation analysis on the plurality of operating variables, and construct an output model of the microgrid group based on the analysis results, the output model including: a photovoltaic output model and a wind power output model; Obtaining a load demand forecast value for a preset time in the future based on a load forecast model for the preset area; generating a scheduling strategy for the microgrid group based on the load demand forecast value and the output model; Performing power supply configuration for the preset area according to the scheduling strategy and acquiring real-time power consumption data, and calculating the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data; The operating variables include: wind power output data Solar energy output data ; The wind energy output data Expressed as ,in, For geographical location The wind energy output value of the wind power generation device; The light output data Expressed as ,in, For geographical location The light energy output value of the photovoltaic power generation device; The performing random correlation analysis on the plurality of operating variables includes: Using the anomalous correlation coefficient Characterize the correlation between the wind energy output data and the solar energy output data: ； ； Wherein, i is the wind energy output data The number of groups, j is the light energy output data The number of groups, is the innocuous correlation coefficient between the wind energy output data and the solar energy output data, and are the nth of the sample matrices of the wind energy output data and the light energy output data, represents the covariance, is the standard deviation; when When , it means that there is no monotonic relationship between the wind power generation device and the photovoltaic power generation device in the preset area; when When , it indicates that the wind power generation device and the photovoltaic power generation device in the preset area are positively correlated with each other; when , it indicates that the wind power generation device and the photovoltaic power generation device in the preset area are negatively correlated with each other. is the correlation threshold; The output model of the microgrid group is expressed as follows: ； Where a and b represent the number of photovoltaic power generation devices and wind power generation devices that meet the constraints, respectively. The constraints are: , is the effective hybrid power of wind and solar, For geographical location The photovoltaic output model, For geographical location Wind power generation model; Before obtaining the load demand forecast value for a preset time in the future based on the load forecast model of the preset area, the method further includes: The historical load demand data of the preset area is divided according to the preset time period, which is expressed as , T is the number of the historical time period; Creating a mapping function ; is the load demand, expressed as ; The mapping function is a time mapping and a weather status mapping of a fixed time period within a preset time period of the preset area.

2. The multi-microgrid system reliability assessment method considering random correlation according to claim 1, characterized in that: The constructing of the microgrid output model based on the analysis results includes: Build geolocation for The photovoltaic output model is expressed as follows: ； in, is the light intensity, is the photovoltaic cell area, is the nominal efficiency, is the temperature of the photovoltaic cell, is the temperature coefficient; Build geolocation for Wind power generation model: ； in, is the rated power of the fan, is the current wind speed, To start the wind speed, is the shutdown wind speed, is the rated wind speed.

3. The multi-microgrid system reliability assessment method considering random correlation according to claim 1, characterized in that: Generating a dispatching strategy for the microgrid group based on the load demand forecast value and the output model includes: Based on the output model, calculating the photovoltaic effective hybrid power of each microgrid in the microgrid group in the current time period, and selecting at least one microgrid that meets the load demand forecast value; In combination with the constraint conditions, the selected at least one microgrid is effectively scheduled.

4. The multi-microgrid system reliability assessment method considering random correlation according to claim 1, characterized in that: The real-time electricity consumption data includes: the total electricity consumption time of users in the preset area, the number of power outages for users, the total power outage time for users, and the cause of the power outage.

5. The multi-microgrid system reliability assessment method considering random correlation according to claim 4, characterized in that: The calculation formula of the dispatching strategy reliability of the microgrid group is as follows: ； Where, is the average number of power outages for power users, is the number of power outages, is the user number, is the total number of users, The total number of power outages for users. is the average power outage duration for power users, The duration of a single power outage. The total duration of power outages for users.

6. The multi-microgrid system reliability assessment method considering random correlation according to claim 5, characterized in that: After calculating the reliability of the dispatching strategy of the microgrid group based on the real-time power consumption data, the method further includes: To reduce the average number of power outages for power users and the average power outage duration for power users For the purpose, the output model of the microgrid group is optimized.

7. A multi-microgrid system reliability assessment system considering random correlation, characterized in that: The reliability assessment method for a multi-microgrid system considering random correlation according to any one of claims 1 to 6 is used to assess the reliability of the multi-microgrid system, comprising: a model building module, configured to obtain a plurality of operating variables of a microgrid group in a preset area, perform random correlation analysis on the plurality of operating variables, and build an output model of the microgrid group based on the analysis results, the output model including a photovoltaic output model and a wind power output model; A load forecasting module, configured to obtain a load demand forecast value for a preset time in the future based on a load forecasting model for the preset area; A strategy generation module, configured to generate a scheduling strategy for the microgrid group based on the load demand forecast value and the output model; A reliability calculation module is used to configure the power supply of the preset area according to the scheduling strategy and obtain real-time power consumption data, and calculate the reliability of the scheduling strategy of the microgrid group based on the real-time power consumption data.

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