Microgrid operation scheduling optimization method based on double-layer adaptive energy management system

Abstract

The application discloses a micro-grid operation scheduling optimization method based on a double-layer adaptive energy management system, and relates to the technical field of micro-grid energy management; the application constructs a basic vector set and a characteristic vector set by processing upper-layer processing equipment operation data, external environment data and large power grid data, determines an optimal scheduling strategy in combination with a historical database, generates an instruction, optimizes micro-grid energy distribution and scheduling from a whole level, and controls equipment in detail according to the scheduling instruction issued by the upper layer to realize accurate operation at a local level; the double-layer structure effectively distinguishes different requirements of global optimization and local control, makes the energy management of the micro-grid more targeted and flexible, and better adapts to a complex operation environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid energy management, and particularly relates to a micro-grid operation scheduling optimization method based on a double-layer adaptive energy management system. BACKGROUND

[0002] As an important part of the smart grid, the micro-grid can realize efficient utilization and flexible scheduling of distributed energy, however, the micro-grid operating environment is complex, containing various distributed power sources, energy storage devices and loads, and its energy management faces many challenges.

[0003] At present, the micro-grid operation scheduling optimization method in the prior art still has the following disadvantages in the actual application process:

[0004] The existing micro-grid energy management system mostly adopts a single-layer structure, which is difficult to effectively distinguish the different needs of global optimization and local control, and has poor flexibility and adaptability;

[0005] At the same time, the prior art lacks efficient use of historical data in the standardization processing of multi-source data and the generation of scheduling strategies, and lacks comprehensive analysis of the economy, environmental protection and power supply reliability of historical data while evaluating historical data, so as to find a scheduling strategy that better matches the current micro-grid state, and the intelligent degree is low.

[0006] Therefore, the micro-grid operation scheduling optimization method based on the double-layer adaptive energy management system is proposed. SUMMARY

[0007] Therefore, the present application provides a micro-grid operation scheduling optimization method based on a double-layer adaptive energy management system to solve the problems raised in the background art.

[0008] The purpose of the present application can be achieved by the following technical scheme: a micro-grid operation scheduling optimization method based on a double-layer adaptive energy management system, comprising:

[0009] Upper-layer global optimization: for equipment operation data, external environment data and large grid data, a basic vector set and a feature vector set are constructed respectively; the constructed basic vector set and feature vector set are used to determine the optimal scheduling strategy of the micro-grid by executing corresponding steps in combination with each group of historical codes in the historical database, and a scheduling instruction is generated; each group of historical codes contains a group of historical scheduling strategies, a historical basic vector set and a historical feature vector set;

[0010] Lower-layer local control: according to the scheduling instruction issued by the upper layer, the equipment is controlled after analyzing the optimal scheduling strategy.

[0011] In some embodiments, the device operation data, external environment data and large power grid data specifically include:

[0012] wherein the device operation data includes output power of the distributed power source, state of charge of the energy storage device and charge-discharge current; the external environment data includes environmental temperature, illumination intensity, wind speed and wind direction; and the large power grid data includes real-time electricity price;

[0013] wherein the basis vector set contains wind direction and real-time electricity price; and the feature vector set contains output power, state of charge, charge-discharge current, environmental temperature, illumination intensity and wind speed.

[0014] In some embodiments, the constructed basis vector set and feature vector set are used to determine the preferred scheduling strategy of the micro-grid by performing corresponding steps on each group of historical encodings in the historical database, specifically as follows:

[0015] For each group of historical encodings in the historical database, a set of to-be-matched historical encodings is screened out based on a set screening rule; for each group of to-be-matched historical encodings, a group of historical feature vector sets is extracted and respectively substituted into the formula with the current constructed feature vector set to perform calculation, thereby obtaining a strategy confidence index of each group of to-be-matched historical encodings .

[0016] For the strategy confidence index of each group of to-be-matched historical encodings , a comparison is made with a set strategy confidence expectation index, and a to-be-matched historical encoding with a strategy confidence index lower than the strategy confidence expectation index is screened out as a preferred encoding;

[0017] If the number of preferred encodings is one, the historical scheduling strategy corresponding to the preferred encoding is directly extracted as the preferred scheduling strategy of the micro-grid;

[0018] If the number of preferred encodings is more than one, the strategy evaluation index Red of each group of preferred encodings is analyzed, a preferred encoding with a higher strategy evaluation index is selected, and the corresponding historical scheduling strategy is extracted as the preferred scheduling strategy of the micro-grid.

[0019] In some embodiments, the to-be-matched historical encodings are screened out based on a set screening rule, specifically as follows:

[0020] For each group of historical codes in the historical database, first, extract each group of historical basic vector set to match the current constructed basic vector set, that is, match the wind direction and real-time electricity price in the current constructed basic vector set with the wind direction and real-time electricity price in each group of historical basic vector set, when the wind direction and real-time electricity price in a group of historical basic vector set are successfully matched, then reserved, traverse all historical codes in the historical database, and the codes that meet the wind direction and electricity price matching success are reserved as the to-be-matched historical codes.

[0021] In some embodiments, the strategy confidence index of each group of to-be-matched historical codes is obtained , and the formula is specifically:

[0022] The formula represents ; wherein Fi represents each group of values in the current constructed feature vector set; Gi represents each group of values in each group of historical feature vectors; i is the number of each group of values, representing output power, state of charge, charge and discharge current, environmental temperature, light intensity and wind speed respectively; and wi is the weight coefficient set for each group of values.

[0023] In some embodiments, the strategy evaluation index Red of each group of preferred codes is analyzed, and the formula is specifically:

[0024] Extract the economic parameter, reliability parameter and environmental protection parameter corresponding to each group of preferred codes, and after comprehensive evaluation, the formula is weighted and calculated to obtain the strategy evaluation index Red of each group of preferred codes; wherein the economic parameter includes fuel consumption cost, power grid interaction power cost and energy storage cycle loss cost; the reliability parameter includes energy storage charge and discharge power fluctuation amplitude and power grid interaction dependence; and the environmental protection parameter includes environmental protection displacement and clean energy proportion.

[0025] In some embodiments, the economic parameter, reliability parameter and environmental protection parameter corresponding to each group of preferred codes are extracted and comprehensively evaluated, and the formula is specifically:

[0026] For fuel consumption cost, power grid interaction power cost and energy storage cycle loss cost, the cumulative value is taken as the operation cost of each group of preferred codes;

[0027] The calculation formula of the energy storage charge and discharge power fluctuation amplitude may be represented as ; P(k) represents the energy storage power at the kth sampling time, the charging is negative and the discharging is positive, P(k) represents the average power in the sampling period, and m is the number of sampling points;

[0028] The calculation formula of the power grid interaction dependence can be represented as y1 represents the power input to the main power grid; y2 represents the total power generation of various power sources; and y3 represents the energy storage discharge power.

[0029] For the fluctuation range of energy storage charging and discharging power, the maximum fluctuation range of each group of historical codes in the historical database is extracted, and the ratio between the fluctuation range of energy storage charging and discharging power and the maximum fluctuation range is calculated to obtain the stable estimate of each group of preferred codes; the independent estimate of each group of preferred codes is obtained by subtracting the grid interaction dependency from the integer;

[0030] The carbon emissions corresponding to the fuel consumption of fuel generators are statistically analyzed and used as the environmental emission of each group of preferred codes; environmentally friendly power sources that are pre-marked are extracted from various power sources; the proportion of environmentally friendly power generation in the total power supply is statistically analyzed and used as the proportion of clean energy in each group of preferred codes.

[0031] In some embodiments, the strategy evaluation index Red for each group of preferred codes is expressed by the formula:

[0032] The operating cost, stable valuation, independent valuation, environmental emissions, and clean energy ratio corresponding to each group of preferred codes are respectively marked as follows: ;

[0033] extract The corresponding reference operating costs, reference stable valuations, reference independent valuations, reference environmental emissions, and reference clean energy percentages are respectively marked as follows: The reference operating cost, reference stable valuation, reference independent valuation, reference environmental emissions, and reference clean energy ratio are all average values ​​of the operating cost, stable valuation, independent valuation, environmental emissions, and clean energy ratio corresponding to each group of preferred codes.

[0034] According to the formula Calculate the strategy evaluation index Red for each group of preferred codes; where The weighting coefficients are set for operating costs, stable valuation, independent valuation, environmental emissions, and the proportion of clean energy, respectively.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This invention constructs a basic vector set and a feature vector set by processing equipment operation data, external environment data, and large power grid data at the upper layer. Combined with historical database, it determines the optimal scheduling strategy and generates instructions, thereby optimizing the energy allocation and scheduling of the microgrid at the overall level. Based on the scheduling instructions issued by the upper layer, the lower layer performs specific control on the equipment to achieve precise operation at the local level. This two-layer structure effectively distinguishes the different needs of global optimization and local control, making the energy management of the microgrid more targeted and flexible, and better able to adapt to complex operating environments.

[0037] The application can quickly find a historical scheduling strategy similar to the current micro-grid state through effective use of historical data, improve the efficiency and accuracy of the scheduling strategy generation.

[0038] The application extracts the economy parameters, reliability parameters and environmental protection parameters of the preferred code, and performs corresponding calculation and processing, substitutes the processed parameters into the formula for weighted calculation, obtains a strategy evaluation index, selects the optimal scheduling strategy by comparing the strategy evaluation index, comprehensively considers the multi-dimensional parameters of economy, reliability and environmental protection, ensures that the selected scheduling strategy is not only similar to the current micro-grid state, but also performs optimally in all aspects, improves the operation efficiency and comprehensive benefits of the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0039] In the following description of exemplary embodiments in conjunction with the accompanying drawings, more details, features and advantages of the application are disclosed, in which:

[0040] Figure 1 The flowchart of the application. DETAILED DESCRIPTION

[0041] Several embodiments of the application will be described in detail with reference to the accompanying drawings, so that those skilled in the art can implement the application. The application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the application comprehensive and complete, and to fully convey the scope of the application to those skilled in the art. The embodiments do not limit the application.

[0042] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of the specification, and should not be interpreted in an idealized or overly formal sense, unless expressly so defined herein.

[0043] Please refer to Figure 1 The micro-grid operation scheduling optimization method based on the double-layer adaptive energy management system is shown, which comprises:

[0044] Data acquisition: Real-time acquisition of equipment operation data, external environment data and large grid data by various sensors;

[0045] Among them, the equipment operation data includes the output power of the distributed power supply, the state of charge of the energy storage device and the charging and discharging current; the external environment data includes the environmental temperature, the light intensity, the wind speed and the wind direction; the large grid data includes the real-time electricity price;

[0046] Upper global optimization: For equipment operation data, external environment data and large grid data, respectively construct a basic vector set and a feature vector set; Among them, the basic vector set contains wind direction and real-time electricity price; The feature vector set contains output power, state of charge, charging and discharging current, environmental temperature, light intensity and wind speed; Using the constructed basic vector set and feature vector set, combined with each group of historical encoding in the historical database, the corresponding steps are executed to determine the optimal scheduling strategy of the microgrid, and the scheduling instruction is generated; Each group of historical encoding contains a group of historical scheduling strategy, historical basic vector set and historical feature vector set; The scheduling strategy includes the output of each distributed power supply, the charging and discharging power of the energy storage and the grid interaction power, etc.;

[0047] It is supplemented that in a microgrid containing photovoltaic, wind power, energy storage and oil-fired generator, the scheduling strategy in each group of historical encoding is [photovoltaic output, wind power output, oil-fired generator output, energy storage charging power, energy storage discharging power, grid interaction power];

[0048] Specifically:

[0049] For each group of historical encoding in the historical database, first extract each group of historical basic vector set and the currently constructed basic vector set for matching, that is, by matching the wind direction and real-time electricity price in the currently constructed basic vector set with the wind direction and real-time electricity price in each group of historical basic vector set, when the wind direction and real-time electricity price in a group of historical basic vector set are successfully matched, it is reserved, and all historical encodings in the historical database are traversed, and the encodings that meet the wind direction and electricity price matching success are reserved as the to-be-matched historical encodings;

[0050] It is supplemented that the wind direction processing adopts independent coding, converts 8 wind directions (east, south, west, north, southeast, southwest, northeast, northwest) into 8-dimensional binary vectors, and performs wind direction matching;

[0051] The electricity price adopts interval matching, sets a matching threshold, for example, 0.1; If the difference between the real-time electricity price in the currently constructed basic vector set and the real-time electricity price in the historical basic vector set is less than the matching threshold, it is considered as successful matching of the electricity price; For example: the current electricity price is 0.8 yuan / kWh, the historical electricity price is 0.75 yuan / kWh, and the difference between the two groups is 0.05<0.1, which is considered as successful matching.

[0052] For each set of historical codes to be matched, a set of historical feature vectors is extracted and substituted into the formula together with the current set of constructed feature vectors to calculate the strategy confidence index of each set of historical codes ;

[0053] The formula is represented as ; wherein Fi represents each set of values in the current set of constructed feature vectors; Gi represents each set of values in each set of historical feature vectors; i is the number of each set of values, representing output power, state of charge, charging and discharging current, environmental temperature, light intensity, and wind speed; and wi is the weight coefficient set for each set of values;

[0054] For each set of historical codes to be matched, a set of historical feature vectors is extracted and substituted into the formula together with the current set of constructed feature vectors to calculate the strategy confidence index of each set of historical codes , respectively, and compared with the set strategy confidence expectation index to screen out the historical codes to be matched with a strategy confidence index lower than the strategy confidence expectation index as the preferred code;

[0055] It is noted that the lower the strategy confidence index , the more similar the historical codes to be matched are to the current microgrid, and setting the strategy confidence expectation index can quickly screen out highly similar historical data;

[0056] If the number of preferred codes is one, the historical scheduling strategy corresponding to the preferred code is directly extracted as the preferred scheduling strategy of the microgrid;

[0057] If the number of preferred codes is greater than one, the strategy evaluation index Red of each set of preferred codes is analyzed, and the preferred code with a higher strategy evaluation index is selected, and the corresponding historical scheduling strategy is extracted as the preferred scheduling strategy of the microgrid;

[0058] The strategy evaluation index Red of each set of preferred codes is analyzed, which is specifically:

[0059] The economic parameters corresponding to each set of preferred codes are extracted, including fuel consumption cost, grid interactive power cost, and energy storage cycle loss cost;

[0060] It is noted that the fuel consumption cost of the fuel generator (fuel price × fuel quantity);

[0061] The grid interactive power cost (purchase cost - sale benefit);

[0062] The energy storage cycle loss cost (energy loss conversion caused by charging and discharging efficiency);

[0063] For the fuel consumption cost, grid interactive power cost, and energy storage cycle loss cost, the cumulative value is taken as the operation cost of each set of preferred codes;

[0064] extracting the reliability parameters corresponding to each group of preferred codes, wherein the reliability parameters include energy storage charging and discharging power fluctuation amplitude and grid interaction dependence;

[0065] energy storage charging and discharging power fluctuation amplitude The calculation formula can be expressed as ; P(k) represents the energy storage power at the kth sampling time, charging is negative and discharging is positive, P(k) represents the average power in the sampling period, and m is the number of sampling points;

[0066] The calculation formula of the grid interaction dependence can be expressed as , wherein y1 represents the power input of the large grid; y2 represents the total power generated by various power sources, and y3 is the energy storage discharging power; the various power sources include but are not limited to photovoltaic, wind power, and oil-fired generator;

[0067] For the energy storage charging and discharging power fluctuation amplitude, the maximum fluctuation amplitude of each group of historical codes in the historical database is extracted, and the ratio between the energy storage charging and discharging power fluctuation amplitude and the maximum fluctuation amplitude is calculated to obtain the stability evaluation of each group of preferred codes; the grid interaction dependence is subtracted by an integer to obtain the independence evaluation of each group of preferred codes;

[0068] It is supplemented that the smaller the stability evaluation result is, the higher the stability is, and the closer the independence evaluation is to 1, the lower the dependence on the large grid is;

[0069] extracting the environmental protection parameters corresponding to each group of preferred codes, wherein the environmental protection parameters include environmental protection discharge and clean energy proportion;

[0070] The carbon emissions corresponding to the fuel consumption of the oil-fired generator are counted as the environmental protection discharge of each group of preferred codes;

[0071] Extracting the pre-marked environmentally friendly power sources in various power sources, such as photovoltaic and wind power; counting the proportion of the power generation of environmentally friendly power sources in the total power supply as the clean energy proportion of each group of preferred codes;

[0072] It is supplemented that the lower the environmental protection discharge is and the higher the clean energy proportion is, the higher the environmental protection degree of the strategy is;

[0073] For the running cost, stability evaluation, independence evaluation, environmental protection discharge, and clean energy proportion corresponding to each group of preferred codes, they are respectively marked as ;

[0074] extracting respectively corresponding to the reference running cost, reference stability evaluation, reference independence evaluation, reference environmental protection discharge, and reference clean energy proportion, which are respectively marked as ; wherein the reference operation cost, the reference stable valuation, the reference independent valuation, the reference environmental protection discharge, and the reference clean energy proportion are average values of the operation cost, the stable valuation, the independent valuation, the environmental protection discharge, and the clean energy proportion corresponding to each group of preferred codes;

[0075] According to the formula The strategy evaluation index Red of each group of preferred codes is calculated; wherein The weight coefficients corresponding to the operation cost, the stable valuation, the independent valuation, the environmental protection discharge, and the clean energy proportion are respectively set;

[0076] It is additionally explained that the history codes to be matched are filtered through the basic vector (wind direction, electricity price), the strategy confidence index is calculated through the feature vector, and finally the optimal strategy is screened in combination with the evaluation index Red, which reduces invalid calculation and improves real-time performance.

[0077] The lower the strategy confidence index is, the more similar the history strategy is to the current state, and the evaluation index Red comprehensively considers the economy, reliability, and environmental protection parameters to ensure that the screened strategy is both “similar” and “optimal”;

[0078] Lower local control: according to the dispatching instruction issued by the upper layer, the equipment is controlled after analyzing the preferred dispatching strategy;

[0079] It is additionally explained that it also includes communication interaction: for data transmission and interaction, a reliable communication protocol (such as TCP / IP, Modbus, etc.) is adopted to ensure real-time and accurate data transmission. At the same time, the data interaction between the upper global optimization layer and external systems (such as large power grid control center, weather forecasting system) is supported to obtain real-time electricity price, weather forecast, and other information, and to provide more comprehensive data support for optimized dispatching;

[0080] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and do not limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A microgrid operation scheduling optimization method based on a double-layer adaptive energy management system, characterized in that, The method comprises the following steps: Upper layer global optimization: for device operation data, external environment data and large power grid data, a basic vector set and a feature vector set are constructed respectively; using the constructed basic vector set and feature vector set, in combination with each group of historical codes in the historical database, corresponding steps are executed to determine the optimal scheduling strategy of the micro-grid, and a scheduling instruction is generated; wherein each group of historical codes contains a group of historical scheduling strategies, a historical basic vector set and a historical feature vector set; The device operation data, the external environment data and the large power grid data specifically comprise: The device operation data comprises the output power of the distributed power supply, the state of charge of the energy storage device and the charging and discharging current; the external environment data comprises the environmental temperature, the light intensity, the wind speed and the wind direction; the large power grid data comprises the real-time electricity price; The basic vector set contains the wind direction and the real-time electricity price; the feature vector set contains the output power, the state of charge, the charging and discharging current, the environmental temperature, the light intensity and the wind speed; The corresponding steps for determining the optimal scheduling strategy of the micro-grid are specifically: For each set of historical codes in the historical database, the first step is to extract the historical base vector set from each set and match it with the currently constructed base vector set. This involves matching the wind direction and real-time electricity price in the currently constructed base vector set with the wind direction and real-time electricity price in each set of historical base vectors. If both wind direction and real-time electricity price match successfully in a particular set of historical base vectors, it is retained. This process is repeated for all historical codes in the historical database; codes that simultaneously satisfy the condition of matching both wind direction and electricity price are retained as historical codes to be matched, and then processed according to the formula... Calculate the strategy confidence index for each group of historical codes to be matched. , where Fi represents the values ​​of each group in the current feature vector set; Gi represents the values ​​of each group in the historical feature vector set; i is the number of each group of values, representing output power, state of charge, charging and discharging current, ambient temperature, light intensity and wind speed respectively; wi is the weight coefficient set for each group of values; For each group of historical codes to be matched, the historical feature vector set of each group is extracted and substituted into the formula with the current constructed feature vector set for calculation to obtain the strategy confidence index of each group of historical codes to be matched ; If the number of optimal codes is one, the historical scheduling strategy corresponding to the optimal code is directly extracted as the optimal scheduling strategy of the micro-grid; Screening out strategy confidence index History codes to be matched, which are lower than the strategy confidence expectation index, as preferred codes; If the number of optimal codes is greater than one, the strategy evaluation index Red of each group of optimal codes is analyzed, the optimal code with a higher strategy evaluation index is selected, and the corresponding historical scheduling strategy is extracted as the optimal scheduling strategy of the micro-grid; The strategy evaluation index Red of each group of optimal codes is analyzed, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted, wherein the economic parameter comprises the fuel consumption cost, the power grid interaction power cost and the energy storage cycle loss cost; the reliability parameter comprises the energy storage charging and discharging power fluctuation amplitude and the power grid interaction dependence; the environmental protection parameter comprises the environmental protection displacement and the clean energy proportion; Lower layer local control: according to the scheduling instruction issued by the upper layer, the optimal scheduling strategy is analyzed, and the device is controlled. For each group, the operating cost, stable valuation, independent valuation, environmental discharge, and clean energy proportion corresponding to the preferred encoding are respectively marked as ; Extract The reference operation cost, the reference stable valuation, the reference independent valuation, the reference environmental protection discharge, and the reference clean energy proportion corresponding to each group of preferred codes are respectively marked as ; wherein the reference operation cost, the reference stable valuation, the reference independent valuation, the reference environmental protection discharge, and the reference clean energy proportion are average values of the operation cost, the stable valuation, the independent valuation, the environmental protection discharge, and the clean energy proportion corresponding to each group of preferred codes. According to the formula The strategy evaluation index Red of each group of preferred codes is calculated; wherein The weight coefficients corresponding to the operating cost, stable valuation, independent valuation, environmental discharge, and clean energy proportion are respectively set The economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter corresponding to each group of optimal codes are extracted and comprehensively evaluated, and the economic parameter, the reliability parameter and the environmental protection parameter 2.The microgrid operation scheduling optimization method based on the double-layer adaptive energy management system according to claim 1, wherein, ​ ​ Energy storage charge and discharge power fluctuation range The calculation formula can be expressed as ; P k represents the energy storage power at the kth sampling time, negative for charging and positive for discharging, P m represents the average power in the sampling period, and m is the number of sampling points; The calculation formula of grid interaction dependency can be expressed as where y1 represents the power input of the large power grid; y2 represents the total power generation of various power sources, and y3 is the discharging power of energy storage. ​ ​ ​ ​

Images