Low-carbon integrated energy system optimization planning method considering clean energy

Abstract

The invention relates to the field of comprehensive energy, and discloses a low-carbon comprehensive energy system optimization planning method considering clean energy, which comprises the steps of determining an optimization planning target, analyzing energy requirements of a system, and determining constraint conditions in physical, technical, economic and environmental aspects; all kinds of energy resources available in the system are evaluated, the potential and availability of all kinds of renewable energy resources are evaluated based on geographical and climate conditions, the supply modes of all kinds of energy resources are analyzed and determined in combination with energy demand, and a multi-energy collaborative supply scheme is provided; establishing a mathematical model of the multi-energy system, simulating the operation condition of the system, evaluating the contribution of each energy form and the influence of the contribution on the stability of the system, and analyzing the influence of different parameter changes on the operation of the system; a comprehensive objective function is constructed according to requirements and objectives of the system, and constraint conditions of system operation are set according to equipment performance, energy efficiency and environmental protection laws and regulations. The method has the advantages of optimizing multi-energy collaborative supply and ensuring stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy, and in particular to a method for optimizing planning of a low-carbon integrated energy system taking clean energy into consideration. Background Art

[0002] With the continuous consumption of traditional fossil energy, the total amount of global energy is gradually decreasing. At the same time, the serious environmental pollution caused by the use of a large amount of fossil energy is becoming increasingly prominent. In China, the current situation of environmental pollution has become so serious that the state needs to attach great importance to it and take resolute measures to strengthen governance. In this governance process, not only huge amounts of human, material and financial resources have been invested, and huge social energy has been consumed, but the severity of environmental pollution has also seriously restricted the development speed and quality of the national economy. This situation not only affects the quality of life of the people, but also has an adverse impact on the country's long-term development and international image.

[0003] In order to reduce the dependence on and use of fossil energy, the use of low-carbon clean energy is the best solution at present. Low carbon mainly includes waste heat sources, waste heat recovery and reuse, etc. Clean energy such as wind energy and solar energy are greatly affected by weather and environmental factors. The intermittent and unstable supply of them poses a challenge to the stable operation of the energy system. Therefore, it is necessary to design a low-carbon integrated energy system optimization planning method that optimizes the coordinated supply of multiple energy sources and ensures the stable operation of the system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a method for optimizing the planning of a low-carbon integrated energy system taking clean energy into consideration, which has the advantage of improving the utilization rate of clean energy and solves the problems in the above-mentioned background technology.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of improving the utilization rate of clean energy, the present invention provides the following technical solution: a method for optimizing and planning a low-carbon integrated energy system considering clean energy, comprising the following steps:

[0008] S1: Define the goal of optimization planning, analyze the energy demand of the system, and identify the physical, technical, economic and environmental constraints;

[0009] Preferably, S1 further includes predicting energy demand in different time periods based on historical data, seasonal changes, economic activities and population growth factors, analyzing the load curves of various energy sources. If the system involves multiple energy forms, it is necessary to analyze the demand characteristics of each energy source and their coordination with each other separately, evaluate the adjustable demand in different time periods, and achieve efficient use of energy through energy storage and load balancing, identify the key drivers of demand changes, and the impact on system scheduling.

[0010] S2: Evaluate the various energy resources available in the system, evaluate the potential and availability of various renewable energy sources based on geographical and climatic conditions, determine the supply methods of various energy sources in combination with energy demand analysis, propose a multi-energy coordinated supply plan, and make reasonable configurations based on the availability and demand curve of energy resources;

[0011] Preferably, S2 further includes quantifying the potential and availability of renewable energy based on geographical conditions and climatic factors when evaluating various types of energy resources available in the system, and combining historical load data, energy consumption characteristics and predicted demand curves, comprehensively considering the complementarity of traditional energy and clean energy, clarifying the supply methods of different energy sources, and optimizing the proportion of clean energy by establishing an energy collaborative supply model, rationally allocating power generation, energy storage and transmission facilities, and formulating a dynamic energy scheduling plan.

[0012] S3: Establish a mathematical model of the multi-energy system, simulate the operation of the system, evaluate the contribution of each energy form and its impact on the system stability, and analyze the impact of different parameter changes on the system operation;

[0013] Preferably, S3 further includes predicting the load demand in the future period based on historical load data and prediction algorithms, including peak and valley loads and seasonal fluctuations; in the power system, the balance between power generation and load demand determines the frequency of the power grid, and the stability of the system frequency is maintained by adjusting the output of the generator, the charging and discharging of the energy storage system, or the load response; the energy storage system can play an important role when the load demand fluctuates, balancing the differences in energy production and consumption through rapid charging and discharging, modeling the conversion efficiency of each energy source, and evaluating the impact of different conversion technologies on system performance.

[0014] S4: Construct a comprehensive objective function based on the system requirements and goals, and set constraints on system operation based on equipment performance, energy efficiency, and environmental regulations;

[0015] Preferably, S4 further includes that economy is one of the cores of multi-energy system optimization. Usually the goal is to minimize the overall cost of the system, including equipment investment, operating cost, and maintenance cost. The total cost of the system is calculated as: TotalCost = ∑(C investment +Coperational +C maintenance +C fuel )

[0016] Where Total Cost is the total system cost, C investment is the initial investment cost, C operational is the operating cost, C maintenance is the maintenance cost, C fuel It’s the energy cost.

[0017] Carbon emissions constraint calculation formula: Carbon Emissions = ∑ i (E generation,i *CF i )

[0018] In the formula, E generation,i is the power generation of the i-th energy source, CF i is the carbon emission factor of the i-th energy source

[0019] Equipment performance and operating capacity constraints, including maximum load, minimum load and operating efficiency of various generators and energy storage devices;

[0020] For generators: P min,i ≤P gen,i ≤P max,i

[0021] Where P min,i and P max,i are the minimum and maximum power generation capacities of generator i respectively.

[0022] For energy storage devices: E min,s ≤E storage,s ≤E max,s

[0023] In the formula, E min,s and E max,s are the minimum and maximum energy storage capacities of the energy storage device s, respectively.

[0024] S5: Through the monitoring system, the usage and operating status of various energy sources are tracked in real time, the ratio of clean energy to traditional energy is adjusted in time, and the real-time data and prediction algorithms are used for optimized scheduling.

[0025] Preferably, S5 further includes optimizing scheduling based on real-time data and prediction results, and the comprehensive objective function is used to measure the optimization goal of the system and minimize the total energy procurement cost, including the electricity purchase cost of clean energy and traditional energy. The calculation formula is as follows:

[0026]

[0027] In the formula, Crenewable (t) and C traditional (t) is the cost of using clean energy and traditional energy at time t;

[0028] Minimize carbon emissions and reduce the use of traditional energy. The calculation formula is as follows:

[0029]

[0030] Where, CF traditional is the carbon emission factor of traditional energy;

[0031] Maximize the energy conversion efficiency of the system, the calculation formula is as follows:

[0032]

[0033] Where P useful (t) is the effective power output at time t, P input(t) is the total energy consumed;

[0034] According to the optimization results of the objective function, the optimal ratio of clean energy and traditional energy in different time periods is obtained. According to the scheduling results, the charging and discharging plan of the energy storage system is determined, and a daily or hourly energy scheduling plan is output, including the power generation of different energy forms, energy use time periods, and the operating status of energy storage equipment.

[0035] (III) Beneficial effects

[0036] Compared with the prior art, the present invention provides a low-carbon integrated energy system optimization planning method considering clean energy, which has the following beneficial effects:

[0037] By establishing a mathematical model of a multi-energy system, simulating the operation of the system, evaluating the contribution of each energy form and its impact on system stability, and analyzing the impact of different parameter changes on system operation. By constructing a comprehensive objective function, set constraints on system operation based on equipment performance, energy efficiency, and environmental regulations. Through the monitoring system, real-time tracking of the use and operation status of various energy sources, timely adjustment of the ratio of clean energy to traditional energy, and optimization and scheduling using real-time data and prediction algorithms. It has the advantage of improving the utilization rate of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] The present invention provides a technical solution: a low-carbon integrated energy system optimization planning method considering clean energy, comprising the following steps:

[0042] S1: Define the goal of optimization planning, analyze the energy demand of the system, and identify the physical, technical, economic and environmental constraints;

[0043] Based on historical data, seasonal changes, economic activities and population growth factors, predict energy demand in different time periods, analyze the load curves of various energy sources, such as electricity load curves, heating or cooling load curves, identify the cyclical, seasonal and sudden changes in demand fluctuations. If the system involves multiple energy forms, such as electricity, heat energy, cold energy, etc., it is necessary to analyze the demand characteristics of each energy source and the coordination between them separately, evaluate the adjustable demand in different time periods, such as load management, demand response, etc., and achieve efficient use of energy through energy storage, load balancing and other means, identify the key drivers of demand changes, such as weather changes, holidays, economic activities, etc., and their impact on system scheduling;

[0044] Physical constraints: Different types of energy, such as solar energy, wind energy, and natural gas, have different production capacities and geographical conditions, and the impact of these limitations on system design must be considered. For example, the power generation of solar energy is affected by sunshine hours and climatic conditions, while wind energy is affected by wind speed and regional layout. Different types of energy production and conversion equipment have different technical parameters and operating limitations. Constraints must be imposed based on factors such as the maximum power generation capacity, minimum start and stop time, and operating efficiency of the equipment. The capacity of the power grid and the charging and discharging capacity of the energy storage equipment are crucial to the stability of the system. The capacity of the power grid transmission lines, the charging and discharging rates of the energy storage equipment, and its life cycle must be considered.

[0045] Technical constraints: There is efficiency loss when energy is converted from one form to another. Technical constraints include the efficiency of different conversion technologies, such as thermoelectric conversion efficiency, photovoltaic conversion efficiency, etc. The choice of energy storage technology and its capacity must meet system requirements and take into account factors such as the charge and discharge rate, life cycle, and cost of the energy storage equipment. It must be ensured that the system can operate stably in the event of load fluctuations or equipment failures. For example, the instability of renewable energy generation needs to be balanced by energy storage equipment or backup power sources.

[0046] Economic constraints: System design needs to consider the cost of initial investment, including equipment procurement, infrastructure construction, installation and commissioning. In addition to the initial investment, the long-term operation and maintenance costs of the system also need to be budgeted. Energy procurement costs are affected by market price fluctuations, especially for traditional energy. The impact of energy price changes on system optimization needs to be considered, especially when prices fluctuate greatly. In the carbon trading market, the cost or incentives of carbon emissions may affect energy choices. System design needs to consider the economic impact of carbon emissions, such as the implementation of policies such as carbon taxes and emission quotas.

[0047] Environmental constraints: According to national or regional environmental regulations, the system's carbon emissions must be controlled within a certain range. The carbon emission factors of different energy forms need to be evaluated to ensure that the system's carbon emissions meet environmental standards. The construction and operation of the system may have an impact on the local ecological environment. For example, the construction of wind farms may affect bird habitats, and hydropower stations may affect aquatic ecology. An environmental impact assessment is required, and measures should be taken to reduce negative impacts. In addition to carbon dioxide, other pollutants such as NOx and SO2 may be emitted during energy production and consumption. Emission limits and control measures for these pollutants should be considered.

[0048] S2: Evaluate the various energy resources available in the system, evaluate the potential and availability of various renewable energy sources based on geographical and climatic conditions, determine the supply methods of various energy sources in combination with energy demand analysis, propose a multi-energy coordinated supply plan, and make reasonable configurations based on the availability and demand curve of energy resources;

[0049] Based on the geographical location, evaluate the intensity of solar radiation and sunshine hours, such as annual sunshine time and radiation data in different regions, and calculate the potential power generation. Analyze the changes in solar energy resources in different seasons, especially the differences between winter and summer, which affect the efficiency of system design. Evaluate the wind speed and wind energy density in different regions and calculate the potential of available wind energy. For example, the site selection of wind turbines should take into account wind speed and wind stability, and seasonal changes in wind speed should also be considered to ensure that the system can cope with wind energy fluctuations. Evaluate hydropower resources, including factors such as water flow, drop, and basin area, calculate the potential for hydropower generation, and evaluate seasonal fluctuations in water sources to ensure that there is still a stable water source in the dry season or extreme climatic conditions. By analyzing the energy demand curve in different time periods, determine the peak hours, trough hours, and seasonal fluctuations of the load, and determine which energy forms can most effectively supplement the load during different peak demand periods. For energy with large demand fluctuations and poor stability, energy storage equipment can be used to balance the supply and demand differences. For energy with better stability, it can be continuously supplied as a base load power source. Based on the volatility of renewable energy, consider using flexible and adjustable supplementary energy for load regulation. Determine the share of each energy source in the overall supply based on the availability, conversion efficiency, and cost of different energy sources. For example, give priority to the use of local renewable energy and adjust backup energy sources according to load demand. Quantify the supply share of each energy source to ensure the balance of the system during peak and trough periods. Design a coordinated scheduling system for energy production and consumption to ensure the reliability and economy of energy supply during different time periods. For example, when solar energy is sufficient during the day, give priority to solar energy; at night or on cloudy days, use energy storage systems to release electricity or enable other base load energy sources. Establish scheduling priorities to ensure efficient use of renewable energy while avoiding energy surpluses or shortages. Combine distributed energy technology to coordinate the design of multiple energy supply points and energy storage facilities to improve the overall reliability and flexibility of the system. Introduce sufficient energy storage systems into the system to adjust load fluctuations and reduce dependence on traditional fossil energy. For example, battery energy storage can provide backup power when wind and solar energy are insufficient.

[0050] To quantify the multi-energy collaborative supply solution, the following methods can be used:

[0051] Calculate the power generation of each energy source in different time periods, compare it with the energy demand curve, and calculate the matching degree and ability of each energy source to meet the load. Use the "matching coefficient" to quantify the load matching degree of each energy source. A high matching coefficient means that the energy source can effectively meet the load demand in most time periods.

[0052] The comprehensive benefits of the collaborative supply solution are evaluated through indicators such as the total cost of the system, carbon emissions, and energy efficiency. For example, the economic feasibility of the solution can be evaluated using life cycle costs, and the total cost and net present value can be calculated.

[0053] Quantify the stability of the system under different load fluctuations, climate change and demand changes. Use indicators such as "energy supply reliability index" or "load fluctuation range" to quantify the stability of the system.

[0054] Evaluate the system's emergency response capabilities in unexpected situations and quantify the effectiveness of flexible scheduling, for example, through scheduling time, energy storage response speed, etc.

[0055] Calculate the carbon emission reduction of each energy supply option relative to traditional fossil energy, quantify the carbon emission factor of each energy source, and ultimately derive the carbon emissions and emission reduction benefits of the entire system.

[0056] The energy supply scheme is optimized based on the mathematical optimization model to find the optimal combination of various energy supply ratios, scheduling strategies, energy storage strategies, etc. The performance and benefits of different schemes are evaluated through the actual operation of the simulation system.

[0057] S3: Establish a mathematical model of the multi-energy system, simulate the operation of the system, evaluate the contribution of each energy form and its impact on the system stability, and analyze the impact of different parameter changes on the system operation;

[0058] Based on historical load data and prediction algorithms, such as time series analysis and machine learning models, the load demand in the future is predicted, including peak and valley loads and seasonal fluctuations. In the power system, the balance between power generation and load demand determines the frequency of the power grid, and the stability of the system frequency is maintained by adjusting the output of the generator, the charging and discharging of the energy storage system, or the load response. The energy storage system can play an important role when the load demand fluctuates, balancing the difference in energy production and consumption through rapid charging and discharging, thereby maintaining the stability of the system. Model the conversion efficiency of each energy source and evaluate the impact of different conversion technologies on system performance. For example, consider the difference in conversion efficiency between renewable energy and traditional energy. According to the system load demand, the availability of energy resources and the conversion efficiency, optimize the supply mode and proportion of each energy form. For example, in the period when renewable energy is abundant, give priority to the use of solar energy, wind energy, etc.; when the supply of non-renewable energy is tight, use the energy storage system for regulation.

[0059] When optimizing a multi-energy system, multiple optimization objectives are usually set, such as minimizing the total system cost, reducing carbon emissions, improving energy efficiency, etc. In the optimization process, it is crucial to quantify different objectives and constraints.

[0060] Quantify the contribution of each energy source to the system through indicators such as power generation, efficiency, cost and carbon emissions. For example, calculate the load response capability, regulation flexibility, total power generation and impact on system stability of each energy form. Use indicators such as frequency fluctuation, load balance, and energy storage system response time to quantify system stability.

[0061] S4: Construct a comprehensive objective function based on the system requirements and goals, and set constraints on system operation based on equipment performance, energy efficiency, and environmental regulations;

[0062] Economic efficiency is one of the core elements of multi-energy system optimization. The goal is usually to minimize the overall cost of the system, including equipment investment, operating cost, and maintenance cost. The total cost of the system is calculated as: Total Cost = ∑(C investment +C operational +C maintenance +C fuel )

[0063] Where Total Cost is the total system cost, C investment is the initial investment cost, C operational is the operating cost, C maintenance is the maintenance cost, C fuel It’s the energy cost.

[0064] Carbon emissions constraint calculation formula: Carbon Emissions = ∑ i (E generation,i *CF i )

[0065] In the formula, E generation,i is the power generation of the i-th energy source, CF i is the carbon emission factor of the i-th energy source

[0066] Equipment performance and operating capacity constraints, including maximum load, minimum load and operating efficiency of various generators and energy storage devices;

[0067] For generators: P min,i ≤P gen,i ≤P max,i

[0068] Where P min,i and P max,i are the minimum and maximum power generation capacities of generator i respectively.

[0069] For energy storage devices: E min,s ≤E storage,s ≤E max,s

[0070] In the formula, E min,s and Emax,s are the minimum and maximum energy storage capacities of the energy storage device s, respectively.

[0071] S5: Through the monitoring system, the usage and operating status of various energy sources are tracked in real time, the ratio of clean energy to traditional energy is adjusted in time, and the real-time data and prediction algorithms are used for optimized scheduling.

[0072] Based on real-time data and forecast results, optimal scheduling is performed. The comprehensive objective function is used to measure the optimization goal of the system and minimize the total energy procurement cost, including the electricity purchase cost of clean energy and traditional energy. The calculation formula is as follows:

[0073]

[0074] In the formula, C renewable (t) and C traditional (t) is the cost of using clean energy and traditional energy at time t;

[0075] Minimize carbon emissions and reduce the use of traditional energy. The calculation formula is as follows:

[0076]

[0077] Where, CF trditional is the carbon emission factor of traditional energy;

[0078] Maximize the energy conversion efficiency of the system, the calculation formula is as follows:

[0079]

[0080] Where P useful (t) is the effective power output at time t, P input(t) is the total energy consumed;

[0081] According to the optimization results of the objective function, the optimal ratio of clean energy and traditional energy in different time periods is obtained. According to the scheduling results, the charging and discharging plan of the energy storage system is determined, and a daily or hourly energy scheduling plan is output, including the power generation of different energy forms, energy use time periods, and the operating status of energy storage equipment.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-carbon integrated energy system optimization planning method considering clean energy, characterized in that: The following steps are involved: S1: Define the goal of optimization planning, analyze the energy demand of the system, and identify the physical, technical, economic and environmental constraints; S2: Evaluate the various energy resources available in the system, evaluate the potential and availability of various renewable energy sources based on geographical and climatic conditions, determine the supply methods of various energy sources in combination with energy demand analysis, propose a multi-energy coordinated supply plan, and make reasonable configurations based on the availability and demand curve of energy resources; S3: Establish a mathematical model of the multi-energy system, simulate the operation of the system, evaluate the contribution of each energy form and its impact on the system stability, and analyze the impact of different parameter changes on the system operation; S4: Construct a comprehensive objective function based on the system requirements and goals, and set constraints on system operation based on equipment performance, energy efficiency, and environmental regulations; S5: Through the monitoring system, the usage and operating status of various energy sources are tracked in real time, the ratio of clean energy to traditional energy is adjusted in time, and the real-time data and prediction algorithms are used for optimized scheduling.

2. A low-carbon integrated energy system optimization planning method considering clean energy according to claim 1, characterized in that: The S1 further includes predicting energy demand in different time periods based on historical data, seasonal changes, economic activities and population growth factors, analyzing the load curves of various energy sources. If the system involves multiple energy forms, it is necessary to analyze the demand characteristics of each energy source and their coordination with each other, evaluate the adjustable demand in different time periods, and achieve efficient use of energy through energy storage and load balancing, identify the key drivers of demand changes, and the impact on system scheduling.

3. The method for optimizing and planning a low-carbon integrated energy system considering clean energy according to claim 1, characterized in that: The S2 further includes that when evaluating the various types of energy resources available in the system, the potential and availability of renewable energy should be quantified based on geographical conditions and climatic factors, and combined with historical load data, energy consumption characteristics and predicted demand curves, the complementarity of traditional energy and clean energy should be comprehensively considered, and the supply methods of different energy sources should be clarified. By establishing an energy collaborative supply model, the proportion of clean energy should be optimized, power generation, energy storage and transmission facilities should be reasonably configured, and a dynamic energy scheduling plan should be formulated.

4. The method for optimizing and planning a low-carbon integrated energy system considering clean energy according to claim 1, characterized in that: The S3 further includes predicting the load demand in the future, including peak and valley loads, and seasonal fluctuations, based on historical load data and prediction algorithms; in the power system, the balance between power generation and load demand determines the frequency of the power grid, and the stability of the system frequency is maintained by adjusting the output of the generator, the charging and discharging of the energy storage system, or the load response; the energy storage system can play an important role when the load demand fluctuates, balancing the differences in energy production and consumption through rapid charging and discharging, modeling the conversion efficiency of each energy source, and evaluating the impact of different conversion technologies on system performance.

5. The method for optimizing and planning a low-carbon integrated energy system considering clean energy according to claim 1, characterized in that: The S4 further comprises Economic efficiency is one of the core elements of multi-energy system optimization. The goal is usually to minimize the overall cost of the system, including equipment investment, operating cost, and maintenance cost. The total cost of the system is calculated as: Total Cost = ∑(C investment +C operational +C maintenance +C fuel ) Where Total Cost is the total system cost, C investment is the initial investment cost, C operational is the operating cost, C maintenance is the maintenance cost, C fuel It’s the energy cost. Carbon emissions constraint calculation formula: Carbon Emissions = ∑ i (E generation,i *CF i ) In the formula, E generation,i is the power generation of the i-th energy source, CF i It is the carbon emission factor of the i-th energy source. It is the performance and operating capacity constraints of the equipment, including the maximum load, minimum load and operating efficiency of various types of generators and energy storage devices; For generators: P min,i ≤P gen,i ≤P max,i Where P min,i and P max,i are the minimum and maximum power generation capacities of generator i respectively. For energy storage devices: E min,s ≤E storage,s ≤E max,s In the formula, E min,s and E max,s are the minimum and maximum energy storage capacities of the energy storage device s, respectively.

6. A low-carbon integrated energy system optimization planning method considering clean energy according to claim 1, characterized in that: The S5 further includes optimizing the scheduling based on the real-time data and the prediction results. The comprehensive objective function is used to measure the optimization goal of the system and minimize the total energy procurement cost, including the electricity purchase cost of clean energy and traditional energy. The calculation formula is as follows: In the formula, C renewable (t) and C traditional (t) is the cost of using clean energy and traditional energy at time t; Minimize carbon emissions and reduce the use of traditional energy. The calculation formula is as follows: Where, CF traditional is the carbon emission factor of traditional energy; Maximize the energy conversion efficiency of the system, the calculation formula is as follows: Where P useful (t) is the effective power output at time t, P input(t) is the total energy consumed; According to the optimization results of the objective function, the optimal ratio of clean energy and traditional energy in different time periods is obtained. According to the scheduling results, the charging and discharging plan of the energy storage system is determined, and a daily or hourly energy scheduling plan is output, including the power generation of different energy forms, energy use time periods, and the operating status of energy storage equipment.

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