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

Abstract

The invention 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. According to the method, a basic vector set and a feature vector set are respectively constructed through upper-layer processing equipment operation data, external environment data and large power grid data, an optimal scheduling strategy is determined in combination with a historical database, an instruction is generated, energy distribution and scheduling of the micro-grid are optimized from the overall level, and energy distribution and scheduling of the micro-grid are optimized according to the scheduling instruction issued by the upper layer. And the lower layer performs specific control on the equipment to realize precise operation at a local level, and the double-layer structure effectively distinguishes different requirements of global optimization and local control, so that the energy management of the micro-grid is more targeted and flexible, and the micro-grid can better adapt to a complex operating environment.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid energy management, and in particular to a microgrid operation scheduling optimization method based on a double-layer adaptive energy management system. Background Art

[0002] As an important component of the smart grid, microgrids can achieve efficient utilization and flexible scheduling of distributed energy. However, the operating environment of microgrids is complex, involving a variety of distributed power sources, energy storage devices and loads, and their energy management faces many challenges.

[0003] At present, the microgrid operation and dispatch optimization methods in the existing technology still have the following shortcomings in practical application: Existing microgrid energy management systems mostly adopt a single-layer structure, which makes it difficult to effectively distinguish between the different needs of global optimization and local control, and has poor flexibility and adaptability. At the same time, existing technologies lack efficient use of historical data in the standardized processing of multi-source data and the generation of scheduling strategies. When evaluating and considering historical data, there is a lack of comprehensive analysis of the economic efficiency, environmental protection and power supply reliability of historical data, so as to find a scheduling strategy that better matches the current microgrid status. The degree of intelligence is low.

[0004] To this end, a microgrid operation and scheduling optimization method based on a two-layer adaptive energy management system is introduced. Summary of the Invention

[0005] In view of this, the present invention provides a microgrid operation scheduling optimization method based on a two-layer adaptive energy management system to solve the problems raised by the above background technology.

[0006] The object of the present invention can be achieved by the following technical solution: a microgrid operation and scheduling optimization method based on a two-layer adaptive energy management system, comprising: Upper-level 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. Using these constructed basic vector sets and feature vector sets, combined with each set of historical codes in the historical database, the corresponding steps are executed to determine the optimal dispatch strategy for the microgrid and generate dispatch instructions. Each set of historical codes contains a set of historical dispatch strategies, a historical basic vector set, and a historical feature vector set. Lower-layer local control: Based on the scheduling instructions issued by the upper layer, the device is controlled after parsing the optimal scheduling strategy.

[0007] In some embodiments, the device operation data, external environment data, and large power grid data specifically include: Equipment operation data includes the output power of distributed power supplies, the state of charge of energy storage devices, and the charge and discharge current; external environment data includes ambient temperature, light intensity, wind speed, and wind direction; and large power grid data includes real-time electricity prices. The basic vector set includes wind direction and real-time electricity price; the feature vector set includes output power, state of charge, charge and discharge current, ambient temperature, light intensity and wind speed.

[0008] In some embodiments, the constructed basic vector set and feature vector set are combined with each set of historical codes in the historical database to perform corresponding steps to determine the optimal scheduling strategy of the microgrid, specifically: For each group of historical codes in the historical database, the historical codes to be matched are screened out based on the set screening rules; for each group of historical codes to be matched, the historical feature vector sets of each group are extracted and respectively 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 ; Strategy confidence index for each set of historical codes to be matched , respectively compared with the set strategy confidence expected index, and screened out the strategy confidence index The historical codes to be matched that are lower than the expected confidence index of the strategy are selected as the preferred codes; If the number of preferred codes is one, the historical dispatch strategy corresponding to the preferred code is directly extracted as the preferred dispatch strategy of the microgrid; If the number of preferred codes is greater than one, the strategy evaluation index Red of each group of preferred codes is analyzed, 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.

[0009] In some embodiments, the historical codes to be matched are screened out based on the set screening rules, specifically: For each group of historical codes in the historical database, first extract each group of historical basic vector sets and match them with the currently constructed basic vector set, 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 sets. When the wind direction and real-time electricity price in a certain group of historical basic vector sets are successfully matched, they are retained. All historical codes in the historical database are traversed, and the codes that meet the successful matching of wind direction and electricity price are retained as historical codes to be matched.

[0010] In some embodiments, the strategy confidence index of each group of historical codes to be matched is obtained. , the specific formula is: Formula ; Where Fi represents each group of values ​​in the currently 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, which respectively represents output power, state of charge, charge and discharge current, ambient temperature, light intensity and wind speed; wi is the weight coefficient set corresponding to each group of values.

[0011] In some embodiments, the analysis of the strategy evaluation index Red of each group of preferred codes is specifically: The economic parameters, reliability parameters, and environmental protection parameters corresponding to each group of preferred codes are extracted, and after comprehensive evaluation, they are inserted into the formula for weighted calculation to obtain the strategy evaluation index Red of each group of preferred codes; the economic parameters include fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost; the reliability parameters include the fluctuation amplitude of energy storage charging and discharging power and grid interaction dependence; the environmental protection parameters include environmentally friendly emission and the proportion of clean energy.

[0012] In some embodiments, the economic parameters, reliability parameters, and environmental parameters corresponding to each set of preferred codes are extracted and comprehensively evaluated, specifically as follows: The fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost are accumulated as the operating cost of each group of optimal codes; Energy storage charging and discharging power fluctuation range The calculation formula can be expressed as ; Indicates the energy storage power at the kth sampling moment, charging is negative and discharging is positive. It represents the average power in the sampling period, and m is the number of sampling points; The calculation formula of grid interaction dependence can be expressed as , where y1 represents the power input from the large power grid; y2 represents the total power generation of various power sources; and y3 is the energy storage discharge power; For the fluctuation amplitude of energy storage charging and discharging power, the maximum fluctuation amplitude of each group of historical codes in the historical database is extracted. The ratio between the fluctuation amplitude of energy storage charging and discharging power and the maximum fluctuation amplitude is calculated to obtain the stable valuation of each group of preferred codes. The independent valuation of each group of preferred codes is obtained by subtracting the grid interaction dependence from the integer one. The carbon emissions corresponding to the fuel consumption of fuel generators are calculated as the environmentally friendly emissions of each group of preferred codes; pre-marked environmentally friendly power sources are extracted from various power sources; the proportion of environmentally friendly power generation in the total power supply is calculated as the proportion of clean energy of each group of preferred codes.

[0013] In some embodiments, the strategy evaluation index Red of each group of preferred codes is obtained, and the formula is expressed as: The operating cost, stable valuation, independent valuation, environmental emission and clean energy ratio corresponding to each group of preferred codes are marked as ; extract The corresponding reference operating costs, reference stable valuations, reference independent valuations, reference environmental emissions, and reference clean energy ratios are marked as The reference operating cost, reference stable valuation, reference independent valuation, reference environmentally friendly emission and reference clean energy ratio are the average values ​​of the operating cost, stable valuation, independent valuation, environmentally friendly emission and clean energy ratio corresponding to each group of preferred codes. According to the formula Calculate the strategy evaluation index Red of each group of preferred coding; The weight coefficients are set corresponding to operating costs, stable valuation, independent valuation, environmental emissions and the proportion of clean energy.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention processes device operation data, external environment data, and large power grid data at the upper layer to construct a basic vector set and a characteristic vector set, respectively. Combined with the historical database, it determines the optimal scheduling strategy and generates instructions, optimizing the energy distribution and scheduling of the microgrid from a holistic perspective. 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 between 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. The present invention constructs a basic vector set and a feature vector set using collected equipment operation data, external environment data, and large power grid data. Based on set screening rules, the method matches the wind direction and real-time electricity price in the basic vector set to screen out historical codes to be matched from the historical database. The historical feature vector set of the historical codes to be matched and the currently constructed feature vector set are substituted into a formula to calculate a strategy confidence index to evaluate the similarity between the historical codes and the current microgrid state. By effectively utilizing historical data, historical scheduling strategies similar to the current microgrid state can be quickly found, thereby improving the efficiency and accuracy of scheduling strategy generation. The present invention extracts the economic parameters, reliability parameters and environmental protection parameters of the preferred code, performs corresponding calculations and processing, substitutes the processed parameters into the formula for weighted calculation, and obtains the strategy evaluation index. The optimal scheduling strategy is selected by comparing the strategy evaluation index, and the multi-dimensional parameters of economy, reliability and environmental protection are comprehensively considered to ensure that the selected scheduling strategy is not only similar to the current microgrid state, but also performs optimally in all aspects of performance, thereby improving the operating efficiency and comprehensive benefits of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0016] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0017] 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 this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0018] See also Figure 1 As shown in FIG, a microgrid operation and dispatch optimization method based on a two-layer adaptive energy management system includes: Data collection: Use multiple sensors to collect equipment operation data, external environment data, and large power grid data in real time; Equipment operation data includes the output power of distributed power supplies, the state of charge of energy storage devices, and the charge and discharge current; external environment data includes ambient temperature, light intensity, wind speed, and wind direction; and large power grid data includes real-time electricity prices. Upper-level 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. The basic vector set includes wind direction and real-time electricity price. The feature vector set includes output power, state of charge, charge and discharge current, ambient temperature, light intensity, and wind speed. The constructed basic vector set and feature vector set are combined with each set of historical codes in the historical database to execute the corresponding steps to determine the optimal dispatching strategy for the microgrid and generate dispatch instructions. Each set of historical codes includes a set of historical dispatching strategies, a historical basic vector set, and a historical feature vector set. The dispatching strategy includes the output of each distributed power source, the charge and discharge power of energy storage, and the grid interaction power. To supplement, in a microgrid that includes photovoltaics, wind power, energy storage, and fuel generators, the dispatch strategy within each group of historical codes is [photovoltaic output, wind power output, fuel generator output, energy storage charging power, energy storage discharging power, grid interaction power]; Specifically: For each set of historical codes in the historical database, first extract each set of historical basic vectors and match them with the currently constructed basic vector set. That is, match 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 set of historical basic vectors. If the wind direction and real-time electricity price in a certain set of historical basic vectors are successfully matched, they are retained. Then, all historical codes in the historical database are traversed, and the codes that meet the requirements of successful wind direction and electricity price matching are retained as the historical codes to be matched. To supplement, the wind direction processing uses independent coding to convert the eight wind directions (east, south, west, north, southeast, southwest, northeast, and northwest) into an 8-dimensional binary vector and perform wind direction matching; The electricity price uses interval matching, with a matching threshold set, such as 0.1. If the difference between the real-time electricity price in the current basic vector set and the real-time electricity price in the historical basic vector set is less than the matching threshold, the electricity price is considered to be matched successfully. For example, if the current electricity price is 0.8 yuan / kWh and the historical electricity price is 0.75 yuan / kWh, and the difference between the two groups is 0.05<0.1, the match is successful. For each set of historical codes to be matched, extract each set of historical feature vectors and substitute them into the formula with the current set of feature vectors to calculate the strategy confidence index of each set of historical codes to be matched. ; Formula ; Where Fi represents each group of values ​​in the currently 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, ambient temperature, light intensity and wind speed respectively; wi is the weight coefficient set corresponding to each group of values; Strategy confidence index for each set of historical codes to be matched , respectively compared with the set strategy confidence expected index, and screened out the strategy confidence index The historical codes to be matched that are lower than the expected confidence index of the strategy are selected as the preferred codes; Supplementary explanation, strategy confidence index The lower the value, the more similar the historical code to be matched is to the current microgrid situation. By setting the strategy confidence expectation index, highly similar historical data can be quickly screened out. If the number of preferred codes is one, the historical dispatch strategy corresponding to the preferred code is directly extracted as the preferred dispatch strategy of the microgrid; If the number of preferred codes is greater than one, the strategy evaluation index Red of each group of preferred codes is analyzed, 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; Analyze the strategy evaluation index Red of each group of preferred coding, specifically: Extracting the economic parameters corresponding to each group of preferred codes, where the economic parameters include fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost; Supplementary explanation: fuel consumption cost of fuel generator (fuel price × fuel volume); Grid interaction power cost (power purchase cost - power sales revenue); Energy storage cycle loss cost (energy loss conversion caused by charging and discharging efficiency); The fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost are accumulated as the operating cost of each group of optimal codes; Extracting the reliability parameters corresponding to each group of preferred codes, where the reliability parameters include the fluctuation amplitude of energy storage charging and discharging power and the degree of grid interaction dependence; Energy storage charging and discharging power fluctuation range The calculation formula can be expressed as ; Indicates the energy storage power at the kth sampling moment, charging is negative and discharging is positive. It represents the average power in the sampling period, and m is the number of sampling points; The calculation formula of grid interaction dependence can be expressed as , where y1 represents the power input from the large power grid; y2 represents the total power generation of various power sources, and y3 is the energy storage discharge power; various power sources include but are not limited to photovoltaic, wind power, and fuel generators; For the fluctuation amplitude of energy storage charging and discharging power, the maximum fluctuation amplitude of each group of historical codes in the historical database is extracted. The ratio between the fluctuation amplitude of energy storage charging and discharging power and the maximum fluctuation amplitude is calculated to obtain the stable valuation of each group of preferred codes. The independent valuation of each group of preferred codes is obtained by subtracting the grid interaction dependence from the integer one. It is additionally noted that smaller stability estimates indicate higher stability, while independent estimates closer to 1 indicate lower reliance on the main power grid; Extracting the environmental protection parameters corresponding to each group of preferred codes, where the environmental protection parameters include environmentally friendly emissions and clean energy proportion; Count the carbon emissions corresponding to the fuel consumption of fuel generators as the environmentally friendly emissions of each group of preferred codes; Extract pre-labeled environmentally friendly power sources from various power sources, such as photovoltaic and wind power generation; calculate the proportion of environmentally friendly power generation in the total power supply, and use this as the proportion of clean energy for each group of preferred codes; To supplement, the lower the environmental emissions and the higher the proportion of clean energy, the more environmentally friendly the strategy is; The operating cost, stable valuation, independent valuation, environmental emission and clean energy ratio corresponding to each group of preferred codes are marked as ; extract The corresponding reference operating costs, reference stable valuations, reference independent valuations, reference environmental emissions, and reference clean energy ratios are marked as The reference operating cost, reference stable valuation, reference independent valuation, reference environmentally friendly emission and reference clean energy ratio are the average values ​​of the operating cost, stable valuation, independent valuation, environmentally friendly emission and clean energy ratio corresponding to each group of preferred codes. According to the formula Calculate the strategy evaluation index Red of each group of preferred coding; The weight coefficients corresponding to operating costs, stable valuation, independent valuation, environmental emissions, and clean energy proportion are set respectively; To supplement, the historical codes to be matched are filtered using basic vectors (wind direction, electricity price), the strategy confidence index is calculated using the feature vector, and finally the optimal strategy is selected based on the evaluation index Red. This process reduces ineffective calculations and improves real-time performance. The lower the strategy confidence index, the more similar the historical strategy is to the current state. The evaluation index Red comprehensively considers economic, reliability, and environmental parameters to ensure that the screening strategy is both "similar" and "optimal"; Lower-layer local control: Based on the scheduling instructions issued by the upper layer, the device is controlled after parsing the optimal scheduling strategy; Additional information also includes communication interaction: for data transmission and interaction, it uses reliable communication protocols (such as TCP / IP, Modbus, etc.) to ensure real-time and accurate data transmission. At the same time, it supports data interaction between the upper global optimization layer and external systems (such as large power grid control centers and weather forecast systems), obtains real-time electricity prices, weather forecasts and other information, and provides more comprehensive data support for optimized scheduling. The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A microgrid operation and dispatch optimization method based on a two-layer adaptive energy management system, characterized in that: include: Upper-level 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. Using these constructed basic vector sets and feature vector sets, combined with each set of historical codes in the historical database, the corresponding steps are executed to determine the optimal dispatch strategy for the microgrid and generate dispatch instructions. Each set of historical codes contains a set of historical dispatch strategies, a historical basic vector set, and a historical feature vector set. Lower-layer local control: Based on the scheduling instructions issued by the upper layer, the device is controlled after parsing the optimal scheduling strategy.

2. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 1 is characterized in that: The equipment operation data, external environment data and large power grid data specifically include: Equipment operation data includes the output power of distributed power supplies, the state of charge of energy storage devices, and the charge and discharge current; external environment data includes ambient temperature, light intensity, wind speed, and wind direction; and large power grid data includes real-time electricity prices. The basic vector set includes wind direction and real-time electricity price; the feature vector set includes output power, state of charge, charge and discharge current, ambient temperature, light intensity and wind speed.

3. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 2 is characterized in that: The constructed basic vector set and characteristic vector set are combined with each set of historical codes in the historical database to perform corresponding steps to determine the optimal dispatching strategy of the microgrid, specifically: For each group of historical codes in the historical database, the historical codes to be matched are screened out based on the set screening rules; for each group of historical codes to be matched, the historical feature vector sets of each group are extracted and respectively 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 ; Strategy confidence index for each set of historical codes to be matched , respectively compared with the set strategy confidence expected index, and screened out the strategy confidence index The historical codes to be matched that are lower than the expected confidence index of the strategy are selected as the preferred codes; If the number of preferred codes is one, the historical dispatch strategy corresponding to the preferred code is directly extracted as the preferred dispatch strategy of the microgrid; If the number of preferred codes is greater than one, the strategy evaluation index Red of each group of preferred codes is analyzed, 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.

4. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 3 is characterized in that: The historical codes to be matched are screened out based on the set screening rules, specifically: For each group of historical codes in the historical database, first extract each group of historical basic vector sets and match them with the currently constructed basic vector set, 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 sets. When the wind direction and real-time electricity price in a certain group of historical basic vector sets are successfully matched, they are retained. All historical codes in the historical database are traversed, and the codes that meet the successful matching of wind direction and electricity price are retained as historical codes to be matched.

5. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 4 is characterized in that: The strategy confidence index of each group of historical codes to be matched is obtained , the specific formula is: Formula ; Where Fi represents each group of values ​​in the currently 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, which respectively represents output power, state of charge, charge and discharge current, ambient temperature, light intensity and wind speed; wi is the weight coefficient set corresponding to each group of values.

6. The microgrid operation and dispatch optimization method based on a two-layer adaptive energy management system according to claim 5 is characterized in that: The strategy evaluation index Red of each group of preferred codes is analyzed as follows: The economic parameters, reliability parameters, and environmental protection parameters corresponding to each group of preferred codes are extracted, and after comprehensive evaluation, they are inserted into the formula for weighted calculation to obtain the strategy evaluation index Red of each group of preferred codes; the economic parameters include fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost; the reliability parameters include the fluctuation amplitude of energy storage charging and discharging power and grid interaction dependence; the environmental protection parameters include environmentally friendly emission and the proportion of clean energy.

7. The microgrid operation scheduling optimization method based on a two-layer adaptive energy management system according to claim 6 is characterized in that: The economic parameters, reliability parameters and environmental protection parameters corresponding to each group of preferred codes are extracted and comprehensively evaluated, specifically: The fuel consumption cost, grid interaction power cost, and energy storage cycle loss cost are accumulated as the operating cost of each group of optimal codes; Energy storage charging and discharging power fluctuation range The calculation formula can be expressed as ; Indicates the energy storage power at the kth sampling moment, charging is negative and discharging is positive. It represents the average power in the sampling period, and m is the number of sampling points; The calculation formula of grid interaction dependence can be expressed as , where y1 represents the power input from the large power grid; y2 represents the total power generation of various power sources; and y3 is the energy storage discharge power; For the fluctuation amplitude of energy storage charging and discharging power, the maximum fluctuation amplitude of each group of historical codes in the historical database is extracted. The ratio between the fluctuation amplitude of energy storage charging and discharging power and the maximum fluctuation amplitude is calculated to obtain the stable valuation of each group of preferred codes. The independent valuation of each group of preferred codes is obtained by subtracting the grid interaction dependence from the integer one. Count the carbon emissions corresponding to the fuel consumption of fuel generators as the environmentally friendly emissions of each group of preferred codes; Extract pre-marked environmentally friendly power from various power sources; The proportion of environmentally friendly power generation in the total power supply is calculated as the proportion of clean energy for the preferred coding of each group.

8. The microgrid operation and dispatch optimization method based on a two-layer adaptive energy management system according to claim 7 is characterized in that: The strategy evaluation index Red of each group of preferred codes is obtained, and the formula is expressed as follows: The operating cost, stable valuation, independent valuation, environmental emission and clean energy ratio corresponding to each group of preferred codes are marked as ; extract The corresponding reference operating costs, reference stable valuations, reference independent valuations, reference environmental emissions, and reference clean energy ratios are marked as ; The reference operating cost, reference stable valuation, reference independent valuation, reference environmental emission and reference clean energy ratio are the average values ​​of the operating cost, stable valuation, independent valuation, environmental emission and clean energy ratio corresponding to each group of preferred codes; According to the formula Calculate the strategy evaluation index Red of each group of preferred coding; The weight coefficients are set corresponding to operating costs, stable valuation, independent valuation, environmental emissions and the proportion of clean energy.

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