Multi-microgrid system reliability evaluation method and system considering random correlation

Abstract

The invention discloses a multi-microgrid system reliability evaluation method and system considering random correlation, and the method comprises the steps: obtaining a plurality of operation variables of a microgrid group in a preset region, carrying out the random correlation analysis of the plurality of operation variables, building an output model of the microgrid group based on an analysis result, and carrying out the evaluation of the reliability of the microgrid group. The output model comprises a photovoltaic output model and a wind energy output model; based on the load prediction model of the preset area, obtaining a load demand prediction value in a future preset time; generating a scheduling strategy of the microgrid group based on the load demand predicted value and the output model; and performing power supply configuration of the preset area according to the scheduling strategy, obtaining real-time power consumption data, and calculating the scheduling strategy reliability of the microgrid group based on the real-time power consumption data. On the basis of considering the difference of the power supply reliability requirements of the loads in the micro-grid group, modeling is carried out on the energy distribution strategy in the micro-grid group, the power supply potential of the micro-grid group is fully excavated, and a foundation is laid for making corresponding operation strategies and optimization 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] The environmental pollution caused by energy consumption is becoming more and more serious. The use of new energy to generate electricity is the future development trend. The combined heat and power system proposed in recent years has become a research hotspot because of its advantages of improving energy utilization, reducing environmental pollution, and renewable resources.

[0003] With the development of microgrid technology, several microgrid projects have been built in various places. In industrial parks and other places, microgrid groups composed of several microgrids have become one of the trends in the development of microgrids. Microgrid groups are more complex systems composed of several microgrids. Compared with microgrids, they have different characteristics. There is energy mutual assistance and support between the microgrids in the microgrid group system.

[0004] However, the power supply reliability of a microgrid group has more complex characteristics than that of a single microgrid, and current microgrid research is mainly conducted on a single microgrid, without considering the situation where multiple microgrids are connected to the distribution network at the same time. This regional multi-microgrid system composed of geographically adjacent microgrids will affect the operation of the microgrid, so in this case, the operating characteristics of multiple microgrids are different from those of a single microgrid. The distributed power configuration and load demand of each microgrid in the region are not consistent. Summary of the invention

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

[0006] In order 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 number of operating variables of a microgrid group in a preset area, perform random correlation analysis on the number of operating variables, and construct an output model of the microgrid group based on the analysis results, wherein the output model includes: a photovoltaic output model and a wind power output model;

[0008] Based on the load forecasting model of the preset area, obtaining a load demand forecast value for a preset time in the future;

[0009] Based on the load demand forecast value and the output model, generating a dispatching strategy for the microgrid group;

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

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

[0013] The light energy output data O l Expressed as in, The geographical location is d l (x l ,y l )’s light energy output value of a 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 light 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 algebraic correlation coefficient between the wind power output data and the solar power output data, R i and R j are the sample matrices of the wind power output data and the solar power output data, respectively, 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 result 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 fan, 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 effective photoelectric 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, 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, the photovoltaic effective hybrid power of each microgrid in the microgrid group in the current time period is calculated, and at least one microgrid that meets the load demand prediction value is selected;

[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 of the dispatching strategy reliability of the microgrid group is as follows:

[0040]

[0041] In the formula, f 1 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, f 2 is the average power outage duration for power supply users, u c is the duration of a single power outage, ∑u c N c The total power outage duration 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] To reduce the average number of power outages for power users 1 and the average power outage duration of power supply users f 2 For the purpose, 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, which is used to obtain a number of operating variables of a 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, wherein the output model includes: a photovoltaic output model and a wind power output model;

[0046] A load forecasting module, which is used to obtain a load demand forecast value at a future preset time based on a load forecasting model of the preset area;

[0047] A strategy generation module, which is used to generate a dispatch 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 so that the at least one processor executes 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 solution of the embodiment of the present invention has 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 in each microgrid in the microgrid group, and the mutual correlation was analyzed from the dimensions of location relationship and power generation type, providing a basis for subsequent scheduling;

[0053] 2. A hybrid processing model of microgrid clusters is established based on correlation analysis, which realizes high-intensity complementarity between photovoltaic and wind energy, fully utilizes resources and improves 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 groups, which not only provides guidance for the coordinated operation and dispatching of microgrid groups, but also ensures high-reliability power supply of microgrid groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 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 It is a block diagram of a module 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] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is 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 only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept 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 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.

[0062] Microgrids are divided according to their geographical locations. Considering the complexity within each microgrid, there may be photovoltaic power generation or wind power generation. Therefore, variables are obtained in advance based on the internal mode of the microgrid, and the correlation between different types of power generation is analyzed through correlation. Based on this, the internal output mode of the microgrid is also analyzed based on the internal mode.

[0063] Step S200, based on the load forecasting model of the preset area, obtain the load demand forecast value at a preset time in the future.

[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, a suitable 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: configure power supply in a 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.

[0067] The power supply configuration is completed according to the scheduling strategy and the 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 forecasting model, the load demand forecast value is obtained, and the dispatching strategy is generated in combination with the output model, which realizes the reasonable power supply configuration planning of the microgrid group and improves the energy utilization efficiency; according to the dispatching strategy, the power supply configuration is carried out and the real-time power consumption data is obtained to calculate the reliability of the dispatching 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 energy output data O w And light 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; light energy output data O l Expressed as in, The geographical location is d l (x l ,y l )’s light energy output value of a photovoltaic power generation device.

[0071] The operating variables in the preset regional microgrid group, that is, the wind power output data O w And light output data l 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 weather, time and other factors. In the output model, the accurate inclusion of light output data can make the model closer to the actual situation and provide more accurate prediction and planning basis for the reliable operation of the microgrid.

[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 acquired 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 epochal 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 energy output data O l The number of groups, ρ ij is the algebraic correlation coefficient between wind power output data and solar power output data, R i and R j are the yndrin of the sample matrices of wind power output data and solar power output data, Cov(·) represents the covariance, and δ(·) is the standard deviation.

[0078] By using the algebraic correlation coefficient ρ to characterize the wind power output data O w The correlation with 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] For the reliability assessment of multi-microgrid systems, it is crucial to accurately grasp the correlation between wind and solar output data. On the one hand, it helps to more fully understand the complementarity or substitutability between different energy sources, so that the synergy of multiple energy sources can be more reasonably considered when constructing the output model. For example, if the wind and solar output data are positively correlated, then in some cases it can be expected that when the wind output is high, the solar output may also be relatively high, which can provide a reference for the formulation of scheduling strategies to fully utilize the advantages of multiple energy sources and improve the reliability and stability of the system. On the other hand, by quantifying the correlation, potential risk factors can also be identified. If the correlation is too high, it may mean that the system is too dependent on a certain energy source. Once there is a problem with this energy source, it may have a greater impact on the operation of the entire microgrid group.

[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 synergy or offset between the two. 0 and ρ<ρ 0 When the positive correlation and negative correlation are determined, it is helpful to take corresponding measures according to different relationship types when formulating scheduling strategies and reliability assessment.

[0085] For the positive correlation case (ρ>ρ 0 ), which means that the wind power generation device and the photovoltaic power generation device have a certain degree of synchronization in output. 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 requirements. For the negative correlation case (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 correlation threshold ρ 0 , providing a clear boundary for judging positive correlation and negative correlation. In practical applications, this threshold can be reasonably determined based on the specific conditions and historical data of the preset area. By analyzing and counting a large amount of data, a threshold suitable for the area can be found to improve the accuracy and reliability of the judgment.

[0087] It should be noted that the correlation threshold ρ in this embodiment is 0 It is a dynamic parameter, that is, it 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 result includes:

[0089] Step S121, constructing a geographic 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 geographic location d w (x w ,y w ) wind power generation model:

[0093]

[0094] Among them, P r is the rated power of the fan, 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 effective photoelectric mixing power.

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

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

[0100] Step S201: divide the historical load demand data of a 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 helps to systematically organize and analyze historical data, making the load demand data of different time periods clearer and more identifiable. By clarifying the numbering of historical time periods, it provides an orderly basis 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] The establishment of this mapping relationship takes into account two important factors: time and weather conditions. Time mapping can reflect the changing patterns of load demand in different time periods, such as the load differences between weekdays and weekends, daytime and nighttime. Weather state mapping takes into account 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 the mapping of time and weather conditions, the load demand forecast value for the preset time in the future can be predicted more accurately, providing a more reliable basis for the formulation of the dispatching strategy of the multi-microgrid system.

[0105] For example, the above time periods can be divided according to months and seasons. If the load in winter and summer increases significantly, the time periods can be divided according to quarters from the perspective of user demand. For example, the rainy season in summer will lead to a decrease in solar power generation; therefore, from the perspective of power supply, the time periods can be divided according to months / days. The mapping relationship in this embodiment is relatively flexible, and the status of the power user and the power supplier in different time periods is taken into account at the same time.

[0106] Therefore, the photovoltaic hybrid power is different in different time periods, especially under the influence of weather. For example, in the rainy season and dry and windless weather. Therefore, according to the time period k mentioned above, the photovoltaic effective hybrid power in the microgrid in the same period is calculated using the output model, and the load demand y that meets the forecast is selected. 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, the comprehensive output of photovoltaic power generation and wind power generation is fully considered, making the selected microgrid more targeted and effective.

[0110] Step S320: effectively schedule 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, that is, it 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, the embodiment of the present invention also includes completing reliability verification through user experience. Specifically, after completing the power supply configuration according to the scheduling strategy and collecting real-time power consumption data during the power supply period, the real-time power consumption data includes: the total power consumption time of users in the preset area, the number of power outages of users, the total power outage time of users, and the cause of the power outage.

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

[0114]

[0115] In the formula, f 1 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 cis the total number of users, ∑λ c N c is the total number of power outages for users, f 2 is the average power outage duration for power supply users, u c is the duration of a single power outage, ∑u c N c The total power outage duration for users.

[0116] f 1 represents the average number of power outages for power users, f 2 The two indicators represent the average power outage duration of power supply users. They can directly reflect the stability and reliability of the microgrid group's power supply to users under the dispatch strategy. By counting and calculating the number of power outages and the duration of power outages for users, we can clearly understand the operation effect of the microgrid group and provide specific data support for evaluating the pros and cons of the dispatch strategy.

[0117] The introduction of parameters such as the number of power outages, user number, and single power outage duration fully considers the actual conditions of different users, avoiding only evaluating from an overall perspective while ignoring individual differences, making the reliability assessment more comprehensive and accurate. For different users, the number of power outages and the duration of power outages may be different. By counting and summing the situation of each user and dividing it by the total number of users, the average indicator reflecting the power supply reliability of the entire microgrid group can be obtained.

[0118] The reliability index obtained by the above calculation formula can help us understand the shortcomings of the current dispatching strategy, so as to optimize and improve it in a targeted manner. For example, if the calculated average number of power outages is high, it means that the dispatching strategy needs to be improved in terms of ensuring power supply stability. We can further analyze the reasons, such as whether it is caused by insufficient energy supply, equipment failure, etc., and take corresponding measures, such as increasing backup power supply, optimizing equipment maintenance, etc., to improve the reliability of the microgrid group.

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

[0120] Step S410, to reduce the average value of power outage times for power supply users 1 and the average power outage duration of power supply users f 2 For this purpose, the output model of the microgrid group is optimized.

[0121] By optimizing the output model of the microgrid group, the reliability of the multi-microgrid system can be further improved. With the continuous accumulation and analysis of real-time power consumption data, problems in the output model can be discovered in a timely manner, and targeted adjustments and improvements can be made. This continuous optimization process can enable the system to continuously adapt to changing power demand and operating environment, improve the adaptability and stability of the system; in addition, reducing the number and duration of power outages can significantly improve the satisfaction of power users. 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, and their trust and reliance on the multi-microgrid system can be enhanced.

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

[0125] A strategy generation module 3, which is used to generate a dispatch 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 the 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 so that the at least one processor executes 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 reliability assessment method and system for a multi-microgrid system taking into account 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 a preset area; generating a dispatching 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 dispatching strategy and obtaining real-time power consumption data, and calculating the dispatching strategy reliability 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 in each microgrid in the microgrid group, and the mutual correlation was analyzed from the dimensions of location relationship and power generation type, providing a basis for subsequent scheduling;

[0132] 2. A hybrid processing model of microgrid clusters is established based on correlation analysis, which realizes high-intensity complementarity between photovoltaic and wind energy, fully utilizes resources and improves 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 groups, which not only provides guidance for the coordinated operation and dispatching of microgrid groups, but also ensures high-reliability power supply of microgrid groups.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant 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 within 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 number of operating variables of a microgrid group in a preset area, perform random correlation analysis on the number of operating variables, and construct an output model of the microgrid group based on the analysis results, wherein the output model includes: a photovoltaic output model and a wind power output model; Based on the load forecasting model of the preset area, obtaining a load demand forecast value for a preset time in the future; Based on the load demand forecast value and the output model, generating a dispatching strategy for the microgrid group; The power supply configuration of the preset area is performed according to the scheduling strategy and real-time power consumption data is obtained, and the scheduling strategy reliability of the microgrid group is calculated based on the real-time power consumption data.

2. The multi-microgrid system reliability assessment method considering random correlation according to claim 1 is characterized in that: The operating variables include: wind energy output data. w And light output data l ; The wind energy output data O w Expressed as in, The geographical location is d w (x w ,y w )’s wind energy output; The light energy output data O l Expressed as in, The geographical location is d l (x l ,y l )’s light energy output value of a photovoltaic power generation device.

3. The multi-microgrid system reliability assessment method considering random correlation according to claim 2 is characterized in that: The performing random correlation analysis on the plurality of operating variables comprises: The correlation coefficient ρ is used to characterize the correlation between the wind energy output data and the light energy output data: 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 algebraic correlation coefficient between the wind power output data and the light power output data, R i and R j are the sample matrices of the wind energy output data and the solar energy output data, respectively, Cov(·) represents the covariance, and δ(·) represents the standard deviation; 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; 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; When ρ<ρ 0 When , 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.

4. The multi-microgrid system reliability assessment method considering random correlation according to claim 1 is characterized in that: The constructing the output model of the microgrid group based on the analysis result includes: Construct geographic location d l (x l ,y l ) is expressed as follows: 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; Construct geographic location d w (x w ,y w ) wind power generation model: Among them, P r is the rated power of the fan, 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; The output model of the microgrid group is expressed as follows: Wherein, 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, and P is the photovoltaic effective hybrid power.

5. The multi-microgrid system reliability assessment method considering random correlation according to claim 4 is characterized in that: 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, expressed as X={x 1 ,x 2 ,…,x T }, T is the number of the historical time period; Create a mapping function f: X→Y; Y is the load demand, expressed as Y={y 1 ,y 2 ,…,y T }; 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.

6. The multi-microgrid system reliability assessment method considering random correlation according to claim 5 is characterized in that: The generating a dispatching strategy of the microgrid group based on the load demand forecast value and the output model includes: Based on the output model, the photovoltaic effective hybrid power of each microgrid in the microgrid group in the current time period is calculated, and at least one microgrid that meets the load demand prediction value is selected; In combination with the constraint conditions, the selected at least one microgrid is effectively scheduled.

7. The multi-microgrid system reliability assessment method considering random correlation according to claim 1 is 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.

8. The multi-microgrid system reliability assessment method considering random correlation according to claim 7 is characterized in that: The calculation formula of the dispatching strategy reliability of the microgrid group is as follows: 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, and u c is the duration of a single power outage, ∑u c N c The total power outage duration for users.

9. The multi-microgrid system reliability assessment method considering random correlation according to claim 8, characterized in that: After calculating the dispatching strategy reliability of the microgrid group based on the real-time power consumption data, the method further includes: With the purpose of reducing the average value f1 of the number of power outages for power supply users and the average value f2 of the power outage duration for power supply users, the output model of the microgrid group is optimized.

10. A multi-microgrid system reliability assessment system considering random correlation, characterized in that: include: A model building module, which is used to obtain a number of operating variables of a 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, wherein the output model includes: a photovoltaic output model and a wind power output model; A load forecasting module, which is used to obtain a load demand forecast value at a future preset time based on a load forecasting model of the preset area; A strategy generation module, which is used to generate a dispatch 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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