Electricity-carbon double-control optimized operation method and device for integrated energy system

Abstract

The invention relates to an electricity-carbon double-control optimized operation method and device for an integrated energy system, and the method comprises the steps: carrying out the energy grade division of each energy supply party of each energy in the integrated energy system, and obtaining an energy grade division result; determining the energy demand of the user, and enabling the energy demand to correspond to the energy grade division result to obtain an industrial integrated energy system optimization operation strategy considering the power and carbon double control; based on the optimized operation strategy of the industrial integrated energy system, constructing a multi-energy collaborative optimization model of the industrial integrated energy system considering power-carbon double control; solving the multi-energy collaborative optimization model of the industrial integrated energy system considering power-carbon double control to obtain each energy optimization scheduling mode in the integrated energy system; and scheduling each energy of the integrated energy system by adopting each energy optimization scheduling mode in the integrated energy system. According to the invention, supply of various energy sources can be realized in a more economical and environment-friendly manner, and conversion and utilization of different energy sources can be realized in a more efficient manner.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and in particular to an electricity-carbon dual-control optimization operation method and device for an integrated energy system. Background Art

[0002] my country has a rich variety of energy with huge reserves, but due to its large population, there is a contradiction between huge energy reserves and low per capita possession; at the same time, the geographical distribution of my country's energy is relatively uneven. In economically developed regions, energy demand is high, but energy reserves are small, while underdeveloped regions have huge energy reserves. Overall, production and construction require high energy supply, while energy is relatively scarce. There is a prominent contradiction between my country's economic development trend and energy supply. Therefore, it is necessary to return traditional energy to the position of basic production capacity and make extensive use of renewable energy and clean energy to support my country's further development.

[0003] However, there are still obstacles to the further promotion and utilization of clean energy. For example, distributed renewable energy has strong randomness in output and is difficult to access and utilize on a large scale. It is necessary to adopt advanced and appropriate energy utilization methods to achieve efficient utilization, flexible storage and consumption of renewable energy. The integrated energy system makes full use of the advantages of various energy sources through the interconnection, flexible use and mutual complementation of energy systems. Compared with traditional energy systems, it can achieve economical and efficient use of various energy sources to a greater extent. At the same time, through the flexible conversion of various energy sources, it can better consume renewable energy with strong randomness.

[0004] At present, a certain number of research results have been achieved in the multi-energy coordinated optimization of integrated energy systems, but they only generally couple multiple energy sources together to plan and dispatch energy supply. In areas with diverse energy load types and high energy consumption, it is difficult to show the outstanding advantages of the economy and efficiency of integrated energy systems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an electric-carbon dual-control optimization operation method and device for an integrated energy system, which can realize the supply of multiple energy sources more economically and environmentally friendly, and realize the conversion and utilization of different energy sources more efficiently.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide an electricity-carbon dual control optimization operation method for a comprehensive energy system, comprising the following steps:

[0007] Carry out energy grade classification for each energy supplier of each energy source in the comprehensive energy system and obtain energy grade classification results;

[0008] Determine the energy demand of the user, match the energy demand with the energy grade classification result, and obtain the optimal operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon;

[0009] Based on the optimized operation strategy of the industrial integrated energy system, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed;

[0010] Solve the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the comprehensive energy system;

[0011] The various energy sources in the comprehensive energy system are scheduled using the optimal scheduling method for each energy source in the comprehensive energy system.

[0012] The energy grade classification of each energy supplier of each energy in the comprehensive energy system is performed to obtain the energy grade classification result, which specifically includes:

[0013] Based on the weights of the energy quality evaluation indicators corresponding to the various energy sources in the comprehensive energy system and the values ​​of the energy quality evaluation indicators in the comprehensive energy system, respectively construct energy quality weighted evaluation matrices corresponding to the various energy sources in the comprehensive energy system;

[0014] Based on the energy grade weighted evaluation matrix corresponding to each energy source in the integrated energy system, the superior and inferior solution distance method is used to determine the relative proximity of each energy supplier in the integrated energy system.

[0015] According to the relative proximity of each energy supplier of each energy in the comprehensive energy system and the preset high and medium grade classification preset values ​​of each energy, the energy supplier of each energy is respectively classified into energy grades to obtain the energy grade classification results.

[0016] The energy demand of the user is determined, and the energy demand is matched with the energy grade classification result to obtain an optimized operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon, specifically including:

[0017] Obtain users’ energy usage data;

[0018] Performing cluster analysis on the energy consumption data to obtain energy demand and typical energy consumption behavior of users;

[0019] Locate the energy quality demand of the user based on the energy demand and typical energy consumption behavior;

[0020] Combining the energy grade classification result with the energy grade demand, an energy supply structure is obtained;

[0021] An energy network model is constructed according to the energy supply structure, and an optimized operation strategy of the industrial integrated energy system is generated based on the energy network model.

[0022] The optimized operation strategy of the industrial integrated energy system includes operation requirements and operation influencing factors. The operation requirements include economic operation requirements and clean operation requirements; the operation influencing factors include basic energy structure, general energy demand and energy cascade utilization.

[0023] The multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon is constructed based on the optimized operation strategy of the industrial comprehensive energy system, specifically including:

[0024] Determining constraint conditions based on the operation influencing factors of the optimized operation strategy of the industrial integrated energy system;

[0025] Based on the operation requirements of the optimized operation strategy of the industrial integrated energy system, an objective function is determined with the maximization of economy and cleanliness as the optimization goals;

[0026] According to the objective function and constraints, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed.

[0027] The constraints include:

[0028] The linear constraint of gas turbine output by grade is expressed as: Among them, p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; p e,gt (t) is the electrical energy output of the gas turbine at time t; p max,e,gt and p min,e,gt are the upper and lower limits of electric energy output respectively; x gt (t) is the dispatch factor of the gas turbine at time t; a 1,gt and a 2,gt These are operating parameters fitted according to the actual conditions of the equipment; R gt is the ramp rate of the gas turbine; Δt is the unit time; p h,gt,mid (t) is the medium-grade thermal energy output of the gas turbine at time t; p h,gt,low (t) is the low-grade thermal energy output of the gas turbine at time t; η h,gt,mid is the energy efficiency coefficient of medium-grade thermal energy output of the gas turbine; η h,gt,low is the energy efficiency coefficient of the low-grade thermal energy output of the gas turbine;

[0029] Distributed photovoltaic operation constraints, expressed as: p e,pv (t) = η e,pv S pv G(t); where p e,pv (t) is the power output of distributed photovoltaic at time t; η e,pv is the energy conversion efficiency of distributed photovoltaics; S pvis the area of ​​the photovoltaic cell; G(t) is the solar radiation intensity per unit area at time t;

[0030] The battery operation constraints are expressed as: Among them, E es (t) is the battery capacity at time t; σ es is the self-discharge coefficient of the battery; E es,min It is the lower limit of the battery's capacity; E es,max is the upper limit of the battery's capacity; p es,ch (t) is the charging power of the battery at time t; p es,ch,min and p es,ch,max are the lower and upper limits of the battery charging power respectively; p es,dis (t) is the discharge power of the battery at time t; p es,dis,min and p es,dis,max are the lower and upper limits of the battery’s discharge power respectively; η es,ch is the charging efficiency of the battery; η es,dis is the discharge efficiency of the battery; x es,ch (t) and x es,dis (t) are the charging scheduling factor and discharging scheduling factor of the battery at time t; N es is the upper limit of the number of times the battery can be charged and discharged, and D represents the total operating time of the battery;

[0031] The operating constraints of the heat storage tank are expressed as: Among them, E hs (t) is the heat storage capacity of the heat storage tank at time t; σ hs is the self-release coefficient of the heat storage tank; E hs,min is the lower limit of the heat storage capacity of the heat storage tank; E hs,max is the upper limit of the heat storage capacity of the heat storage tank; p hs,ch (t) is the charging power of the heat storage tank at time t; p hs,ch,min and p hs,ch,max are the lower and upper limits of the charging power of the heat storage tank respectively; p hs,dis (t) is the energy release power of the heat storage tank; p hs,dis,min and p hs,dis,max are the lower and upper limits of the energy release power of the heat storage tank respectively; η hs,ch is the charging efficiency of the heat storage tank; η hs,dis is the energy release efficiency of the heat storage tank; x hs,ch (t) and x hs,dis (t) are the charging scheduling factor and discharging scheduling factor of the heat storage tank at time t; N hs It is the upper limit of the number of times the heat storage tank can be charged and discharged;

[0032] The energy conversion equipment operation constraints are expressed as: Among them, poutput (t) is the output power of the energy conversion device at time t; p input (t) is the input power of the energy conversion device at time t; η cov is the conversion efficiency of the energy conversion device; p output,max is the upper limit of output power; p output,min is the lower limit of output power;

[0033] The elastic electric load constraint is expressed as: Among them, L e,mov (t) is the elastic load of electric energy in the industrial park at time t; L e,mov,min (t) and L e,mov,max (t) are the upper and lower limits of the electric energy elastic load at time t respectively;

[0034] The electric vehicle charging load constraint is expressed as: Where I is the number of electric vehicles, J is the number of charging piles, and t i,start is the parking start time of the i-th electric car; t i,end is the parking end time of the i-th electric vehicle; SOC i (t i,start ) is the initial charge state of the ith electric vehicle when it is parked in the parking lot; E vc,i represents the battery capacity of the i-th car; p vc,charge Represents the electric energy provided by a single charging pile to an electric vehicle; x vc,i,j (t) represents the connection status between the i-th electric vehicle and the j-th charging pile; P vc,i is the charging demand of the i-th vehicle; L vc,charge (t) is the sum of the electric energy output of all charging piles at time t;

[0035] The power demand constraint of the data center server is expressed as: Among them, p dc,e (t) is the power load of the data center at time t; p I is the power consumption of the server in the data center when it is not loaded; μ dc (t) is the server utilization rate of the data center at time t; p P is the power consumption of the data center server under full load; m is the number of servers in the data center; μ dc,min To meet the minimum server utilization rate to complete the computing task; delay,max The maximum delay time for processing tasks;

[0036] The heat load constraint is expressed as: Among them, k wall is the heat dissipation coefficient of the building's exterior wall; S wall is the building's exterior wall area; k winis the heat dissipation coefficient of the building's exterior windows; S win is the area of ​​the building's exterior windows; G(t) is the solar radiation intensity per unit area at time t; S c is the building shading coefficient; p in (t) is the calorific value of indoor objects in the building at time t; L ACL,h (t) is the temperature control load generated by the building heating at time t; L ACL,c (t) is the temperature control load generated by the building cooling at time t; ρ is the air density; V air is the volume of air in the building; C air is the specific heat capacity of air; T out (t) is the ambient temperature at time t; T in (t) is the indoor set temperature at time t; T in,min and T in,max They are the lower and upper limits of the indoor set temperature respectively;

[0037] The high-grade and medium-grade energy utilization and recovery operation constraints are expressed as: p steam,low (t) = η steam L steam,mid (t), where L steam,mid (t) is the medium grade heat energy load at time t; p steam,low (t) is the low-grade heat energy recovered at time t; η steam is the heat recovery efficiency;

[0038] The energy supply and demand balance constraint of electric energy cascade utilization is expressed as: Among them, L e (t) is the production load with strong rigidity and high demand for electric energy quality at time t; p e,input (t) is the power output from the distribution network, gas turbines, and distributed photovoltaics at time t to meet the power input of other living loads, electric vehicle charging loads, and energy conversion loads; p eb (t) is the electric energy purchased from the power grid at time t; p es (t) is the redundant electricity sold by the industrial park to the grid at time t;

[0039] The energy supply and demand balance constraint of thermal energy cascade utilization is expressed as: Among them, L h (t) is the common low-grade heat energy load for production and life at time t; p h,output (t) is the thermal energy output of the energy conversion device at time t; p h,input (t) is the heat energy input to the energy conversion device at time t;

[0040] The supply and demand balance constraint of cold energy cascade utilization is expressed as: p c,output (t) = L c (t)+LACL,c (t), where p c,output (t) is the cooling energy output of the energy conversion device at time t; L c (t) is the low-grade cooling energy load for ordinary production and life at time t.

[0041] The objective function f is expressed as: in, The cost of purchasing energy for the comprehensive energy system of the industrial park, including the fuel cost of purchasing natural gas and the cost of purchasing electricity generated by electricity trading with the power grid; g (t) is the natural gas price at time t; p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; c eb (t) is the electricity price of the power grid at time t; c es (t) is the price at which users sell electricity to the grid at time t; p eb (t) is the user purchasing electricity from the grid; p es (t) is the electric energy sold by the user to the grid at time t; The operation and maintenance costs incurred during the application of energy production, storage and conversion equipment built and configured in the park's comprehensive energy system; z,om is the operation and maintenance cost per unit output of the zth device, p z (t) is the output of the zth device at time t, and Z is the total number of devices in the park's integrated energy system; is the cost of carbon tax; c ctax is the unit carbon tax; e gas is the unit carbon dioxide emission rate of natural gas; e grid is the unit carbon dioxide emission rate of electricity production; η grid It is the energy transmission efficiency of the power system.

[0042] The technical solution adopted by the present invention to solve the technical problem is: to provide an electric-carbon dual-control optimization operation device for a comprehensive energy system, comprising:

[0043] A classification module is used to classify the energy grades of various energy suppliers in the comprehensive energy system and obtain energy grade classification results;

[0044] An optimization module is used to determine the energy demand of users, match the energy demand with the energy grade classification result, and obtain an optimized operation strategy for the industrial comprehensive energy system considering the dual control of electricity and carbon;

[0045] A construction module is used to construct a multi-energy collaborative optimization model of an industrial comprehensive energy system taking into account the dual control of electricity and carbon based on the optimized operation strategy of the industrial comprehensive energy system;

[0046] A solution module is used to solve the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the comprehensive energy system;

[0047] The scheduling module is used to schedule various energy sources in the comprehensive energy system by adopting the optimization scheduling method of various energy sources in the comprehensive energy system.

[0048] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for optimizing operation of dual-control of electricity and carbon for an integrated energy system when executing the computer program.

[0049] The technical solution adopted by the present invention to solve its technical problem is: to provide a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned electric-carbon dual-control optimization operation method for an integrated energy system are implemented.

[0050] Beneficial Effects

[0051] Due to the adoption of the above-mentioned technical scheme, compared with the prior art, the present invention has the following advantages and positive effects: the present invention divides energy grades, corresponds energy demand and energy grade division results, obtains the optimal operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon, and constructs a multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon based on the optimal operation strategy of the industrial comprehensive energy system; facing the dual control of electricity and carbon, the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon is solved, and the optimal scheduling mode of each energy in the comprehensive energy system is obtained. This mode comprehensively and comprehensively considers the "quality and quantity" of energy, divides the energy grades of various energy sources mainly including electricity, heat and gas, and forms a comprehensive energy utilization mode of dual control of electricity and carbon with "matching grades" on both sides of energy and load, which reduces carbon emissions, improves the economy and cleanliness of energy use in the park, and more efficiently realizes the conversion and utilization of different energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the first embodiment of the present invention for the electricity-carbon dual control optimization operation method of the integrated energy system. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0054] The first embodiment of the present invention relates to an electricity-carbon dual control optimization operation method for a comprehensive energy system, such as Figure 1 As shown, the following steps are included:

[0055] Step 1: classify the energy grades of each energy supplier in the integrated energy system to obtain the energy grade classification results. This step specifically includes:

[0056] Based on the weights of the energy quality evaluation indicators corresponding to each energy source in the comprehensive energy system and the values ​​of each energy quality evaluation indicator in the comprehensive energy system, the energy quality weighted evaluation matrix corresponding to each energy source in the comprehensive energy system is constructed respectively;

[0057] Based on the energy grade weighted evaluation matrix corresponding to each energy source in the integrated energy system, the superior and inferior solution distance method is used to determine the relative proximity of each energy supplier in the integrated energy system.

[0058] The energy suppliers of each energy are graded according to the relative proximity of each energy supplier in the integrated energy system and the preset high and medium grade classification values ​​of each energy.

[0059] The energy grade classification results include high grade, medium grade and low grade of each energy.

[0060] Specifically, this implementation method can be based on the indicator weighting theory and multi-objective decision-making method to form an energy grade classification algorithm that can comprehensively analyze the evaluation of each indicator and realize the intuitive classification of energy grades. The algorithm is combined with the indicator system to obtain a variety of energy grade classification mechanisms.

[0061] Step 2: determine the energy demand of the user, match the energy demand with the energy grade classification result, and obtain the optimized operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon. This step specifically includes:

[0062] Obtain energy consumption data of users in industrial parks;

[0063] Performing cluster analysis on the energy consumption data to obtain energy demand and typical energy consumption behavior of users in the industrial park;

[0064] Locate the energy quality demand of the user based on the energy demand and typical energy consumption behavior;

[0065] Combining the energy grade classification result and the energy grade demand, a corresponding energy supply structure is obtained;

[0066] An energy network model is constructed according to the energy supply structure, and an optimized operation strategy of the industrial energy system is generated based on the energy network model.

[0067] In some embodiments, the optimized operation strategy of the industrial energy system includes: operation requirements and operation influencing factors. The operation requirements include economic operation requirements and clean operation requirements; the operation influencing factors include basic energy structure, general energy demand and energy cascade utilization.

[0068] This implementation method matches different grades of energy with different energy utilization difficulties and energy conversion efficiencies with industrial park loads that have different requirements for energy supply reliability and energy conversion efficiency, and derives an energy supply strategy for the industrial integrated energy system of "matching grades and cascade utilization".

[0069] Specifically, from the perspective of the overall energy supply of the integrated energy system, natural gas has the widest application, which can generate electricity and heat, due to the wide range of energy uses; electricity can generate heat and also provide refrigeration; thermal energy can only provide refrigeration in addition to heating. Therefore, there is a clear ranking of gas, electricity, and heat. However, within the scope of electricity and heat, due to their different sources and forms, there will be differences in the energy supply effect. Therefore, it is necessary to establish a comprehensive energy grade classification index system based on the different characteristics of electricity and thermal energy, which is used to further subdivide the quality of electricity and thermal energy in the integrated energy system.

[0070] Step 3: Based on the optimized operation strategy of the industrial integrated energy system, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed. This step specifically includes:

[0071] Determining constraint conditions based on the operation influencing factors of the optimized operation strategy of the industrial integrated energy system;

[0072] Based on the operation requirements of the optimized operation strategy of the industrial integrated energy system, an objective function is determined with the maximization of economy and cleanliness as the optimization goals;

[0073] According to the objective function and constraints, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed.

[0074] The constraints include:

[0075] The linear constraint of gas turbine output by grade is expressed as: Among them, p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; p e,gt (t) is the electrical energy output of the gas turbine at time t; p max,e,gt and p min,e,gt are the upper and lower limits of electric energy output respectively; x gt (t) is the dispatch factor of the gas turbine at time t, which is a 0-1 Boolean variable. It takes the value of 1 when the gas turbine starts to output power, otherwise it takes the value of 0; a 1,gt and a 2,gtAll of them are operating parameters fitted according to the actual situation of the equipment and are fixed parameters; R gt is the ramp rate of the gas turbine; Δt is the unit time; p h,gt,mid (t) is the medium-grade thermal energy output of the gas turbine at time t; p h,gt,low (t) is the low-grade thermal energy output of the gas turbine at time t; η h,gt,mid is the energy efficiency coefficient of medium-grade thermal energy output of the gas turbine; η h,gt,low It is the energy efficiency coefficient of the low-grade thermal energy output of the gas turbine.

[0076] Distributed photovoltaic operation constraints, expressed as: p e,pv (t) = η e,pv S pv G(t); where p e,pv (t) is the power output of distributed photovoltaic at time t; η e,pv is the energy conversion efficiency of distributed photovoltaics; S pv is the area of ​​the photovoltaic cell; G(t) is the solar radiation intensity per unit area at time t.

[0077] The battery operation constraints are expressed as: Among them, E es (t) is the battery capacity at time t; σ es is the self-discharge coefficient of the battery; E es,min It is the lower limit of the battery's capacity; E es,max is the upper limit of the battery's capacity; p es,ch (t) is the charging power of the battery at time t; p es,ch,min and p es,ch,max are the lower and upper limits of the battery charging power respectively; p es,dis (t) is the discharge power of the battery at time t; p es,dis,min and p es,dis,max are the lower and upper limits of the battery’s discharge power respectively; η es,ch is the charging efficiency of the battery; η es,dis is the discharge efficiency of the battery; x es,ch (t) and x es,dis (t) are the charging scheduling factor and discharging scheduling factor of the battery at time t, respectively. They are both 0-1 Boolean variables. The same battery can only be in one of the charging state or discharging state at the same time, or it can be not working. Therefore, the sum of the two scheduling factors is greater than 0 and less than 1; N es It is the upper limit of the number of times the battery can be charged and discharged, and D represents the total operating time of the battery.

[0078] The operating constraints of the heat storage tank are expressed as: Among them, E hs (t) is the heat storage capacity of the heat storage tank at time t; σhs is the self-release coefficient of the heat storage tank; E hs,min is the lower limit of the heat storage capacity of the heat storage tank; E hs,max is the upper limit of the heat storage capacity of the heat storage tank; p hs,ch (t) is the charging power of the heat storage tank at time t; p hs,ch,min and p hs,ch,max are the lower and upper limits of the charging power of the heat storage tank respectively; p hs,dis (t) is the energy release power of the heat storage tank; p hs,dis,min and p hs,dis,max are the lower and upper limits of the energy release power of the heat storage tank respectively; η hs,ch is the charging efficiency of the heat storage tank; η hs,dis is the energy release efficiency of the heat storage tank; x hs,ch (t) and x hs,dis (t) are the charging scheduling factor and discharging scheduling factor of the heat storage tank at time t. Similar to batteries, they are 0-1 Boolean variables. The same heat storage tank can only be in one of the charging state or discharging state at the same time, or it can not work. Therefore, the sum of the two scheduling factors is also greater than 0 and less than 1; N hs It is the upper limit of the number of times the heat storage tank can be charged and discharged.

[0079] The energy conversion equipment operation constraints are expressed as: Among them, p output (t) is the output power of the energy conversion device at time t; p input (t) is the input power of the energy conversion device at time t; η cov is the conversion efficiency of the energy conversion device; p output,max is the upper limit of output power; p output,min is the lower limit of output power.

[0080] Operational constraints on electricity and thermal loads in industrial parks, including elastic electricity load constraints, electric vehicle charging load constraints, data center server electricity demand constraints, and thermal load constraints.

[0081] Among them, the elastic electric load constraint is expressed as: Among them, L e,mov (t) is the elastic load of electric energy in the industrial park at time t; L e,mov,min (t) and L e,mov,max (t) are the upper and lower limits of the electric energy elastic load at time t respectively; the total of the electric energy elastic load is 0 throughout the day.

[0082] The electric vehicle charging load constraint is expressed as: Where I is the number of electric vehicles, J is the number of charging piles, and t i,start is the parking start time of the i-th electric car; t i,endis the parking end time of the i-th electric vehicle; SOC i (t i,start ) is the initial charge state of the ith electric vehicle when it is parked in the parking lot; E vc,i represents the battery capacity of the i-th car; p vc,charge Represents the electric energy provided by a single charging pile to an electric vehicle; x vc,i,j (t) represents the connection status between the i-th electric vehicle and the j-th charging pile; P vc,i is the charging demand of the i-th vehicle; L vc,charge (t) is the sum of the electrical energy output by all charging piles at time t.

[0083] The power demand constraint of the data center server is expressed as: Among them, p dc,e (t) is the power load of the data center at time t; p I is the power consumption of the server in the data center when it is not loaded; μ dc (t) is the server utilization rate of the data center at time t; p P is the power consumption of the data center server under full load; m is the number of servers in the data center; μ dc,min To meet the minimum server utilization rate to complete the computing task; delay,max The maximum delay time for processing a task.

[0084] The heat load constraint is expressed as: Among them, k wall is the heat dissipation coefficient of the building's exterior wall; S wall is the building's exterior wall area; k win is the heat dissipation coefficient of the building's exterior windows; S win is the area of ​​the building's exterior windows; G(t) represents the solar radiation intensity per unit area at time t; S c is the building shading coefficient; p in (t) is the calorific value of indoor objects in the building at time t; L ACL,h (t) is the temperature control load generated by the building heating at time t; L ACL,c (t) is the temperature control load generated by the building cooling at time t; ρ is the air density; V air is the volume of air in the building; C air is the specific heat capacity of air; T out (t) is the ambient temperature at time t; T in (t) is the indoor set temperature at time t; T in,min and T in,max They are the lower and upper limits of the indoor set temperature respectively.

[0085] The high-grade and medium-grade energy utilization and recovery operation constraints are expressed as: p steam,low(t) = η steam L steam,mid (t), where L steam,mid (t) is the medium grade heat energy load at time t; p steam,low (t) is the low-grade heat energy recovered at time t; η steam is the heat recovery efficiency.

[0086] Supply and demand balance constraints for natural gas, electricity, heat and cooling energy in industrial parks.

[0087] Among them, the energy supply and demand balance constraint of electric energy cascade utilization is expressed as: Among them, L e (t) is the production load with strong rigidity and high demand for electric energy quality at time t; p e,input (t) is the power output from the distribution network, gas turbines, and distributed photovoltaics at time t to meet the power input of other living loads, electric vehicle charging loads, and energy conversion loads; p eb (t) is the electric energy purchased from the power grid at time t; p es (t) is the redundant electricity sold by the industrial park to the grid at time t.

[0088] The energy supply and demand balance constraint of thermal energy cascade utilization is expressed as: Among them, L h (t) is the common low-grade heat energy load for production and life at time t; p h,output (t) is the thermal energy output of the energy conversion device at time t; p h,input (t) is the thermal energy input of the energy conversion device at time t.

[0089] The supply and demand balance constraint of cold energy cascade utilization is expressed as: p c,output (t) = L c (t)+L ACL,c (t), where p c,output (t) is the cooling energy output of the energy conversion device at time t; L c (t) is the low-grade cooling energy load for ordinary production and life at time t.

[0090] Through the above constraints in the collaborative optimization operation model, the supply of natural gas, electricity, heat energy, and cold energy is optimized and sorted for cascade utilization. Based on the overall relationship between the energy flow grades of natural gas, electricity, heat energy, and cold energy, which decrease step by step, in the cascade utilization within the energy flow, it is determined that medium and high-grade energy will give priority to the energy needs of users with precision production tasks in the park, and the remaining energy and low-grade energy will meet the energy needs of ordinary life purposes, completing the cascade supply of a single energy flow. Redundant energy is supplied as energy of a lower grade through energy conversion equipment, realizing the cascade utilization of multiple energy flows under multi-energy coupling.

[0091] In addition, the linear constraint of gas turbine output grade reflects the model improvement of the actual energy grade output of comprehensive energy production equipment under the energy cascade utilization. At the same time, the start-stop state and climbing state of the equipment in the actual operation process are fully considered, and the modeling refinement of the physical model of energy equipment is increased.

[0092] The energy supply and demand balance constraint of the cascade utilization of electric energy further considers the recovery and conversion of high-grade and medium-grade energy into low-grade energy for reuse within the framework of cascade utilization of energy, deepening the role of cascade utilization of energy in improving energy utilization efficiency.

[0093] The objective function of the collaborative optimization operation model is:

[0094] in, The cost of purchasing energy for the comprehensive energy system of the industrial park, including the fuel cost of purchasing natural gas and the cost of purchasing electricity generated by electricity trading with the power grid; g (t) is the natural gas price at time t; p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; c eb (t) is the electricity price of the power grid at time t; c es (t) is the price at which users sell electricity to the grid at time t; p eb (t) is the user purchasing electricity from the grid; p es (t) is the electric energy sold by the user to the grid at time t.

[0095] It is the operation and maintenance cost of the energy production, storage and conversion equipment built and configured in the park's comprehensive energy system during the application process; among them, c z,om is the operation and maintenance cost per unit output of the zth device, p z (t) is the output of the zth device at time t, and Z is the total number of devices in the park's integrated energy system.

[0096] The carbon tax fee is added to consider the economic efficiency of energy system operation and unify the dimension of optimization objective function; where c ctax is the unit carbon tax; e gas is the unit carbon dioxide emission rate of natural gas; e grid is the unit carbon dioxide emission rate of electricity production; η grid It is the energy transmission efficiency of the power system.

[0097] The above objective function, in order to consider the economic efficiency of energy system operation and unify the dimension of the objective function, adds the calculation of carbon tax costs. Under the guidance of the optimization intention of taking into account the economic efficiency of system operation and the cleanness of system energy use, the collaborative optimization model aims to minimize the daily operating costs of the comprehensive energy system of the industrial park and the carbon tax costs of energy use emissions, and finally obtains the equipment dispatch output strategy and controllable load adjustment strategy in the industrial park, so as to improve the economic efficiency and cleanliness of the park operation.

[0098] At the same time, in terms of energy supply architecture, the energy supply architecture is further refined to meet the energy quality and quantity on the basis of meeting the demand for energy quantity. By establishing the supply and demand balance constraint of high, medium and low grade energy demand being met in sequence, the mathematical modeling expression of the energy supply architecture of the industrial park's energy cascade utilization is realized. At the same time, by improving and integrating the energy equipment energy grade output model and load energy recovery model, the applicability of energy equipment and energy load steady-state modeling in the multi-energy collaborative optimization model of the industrial park comprehensive energy system taking into account the dual control of electricity and carbon is further improved.

[0099] Step 4, solve the multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the integrated energy system. In this step, the multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is solved by the Yalmip+Gurobi joint solution method. After the solution, an energy supply strategy for the industrial park integrated energy system can be generated that takes into account both the optimal system operation economy and the optimal system energy cleanliness.

[0100] Step 5: Use the optimal scheduling method for each energy source in the integrated energy system to schedule each energy source in the integrated energy system.

[0101] The multi-energy collaborative optimization method based on dual control of electricity and carbon provided in this embodiment can achieve the best comprehensive operation effect of the comprehensive energy system of the industrial park: the cost of purchasing natural gas and electricity in the industrial park is the lowest (from an economic perspective); the equipment operation and maintenance costs incurred in the process of production, storage and conversion of various energy sources by the energy equipment built and configured by the industrial park are the lowest (from an economic perspective); the carbon tax levied on carbon dioxide emissions during energy utilization in the industrial park is the lowest (from an environmental perspective).

[0102] The second embodiment of the present invention relates to an electricity-carbon dual control optimization operation device for an integrated energy system, comprising:

[0103] A classification module is used to classify the energy grades of various energy suppliers in the comprehensive energy system and obtain energy grade classification results;

[0104] An optimization module is used to determine the energy demand of users, match the energy demand with the energy grade classification result, and obtain an optimized operation strategy for the industrial comprehensive energy system considering the dual control of electricity and carbon;

[0105] A construction module is used to construct a multi-energy collaborative optimization model of an industrial comprehensive energy system taking into account the dual control of electricity and carbon based on the optimized operation strategy of the industrial comprehensive energy system;

[0106] A solution module is used to solve the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the comprehensive energy system;

[0107] The scheduling module is used to schedule various energy sources in the comprehensive energy system by adopting the optimization scheduling method of various energy sources in the comprehensive energy system.

[0108] The division module comprises:

[0109] A matrix construction unit, for constructing a weighted evaluation matrix of energy quality corresponding to each energy in the comprehensive energy system based on the weight of the energy quality evaluation index corresponding to each energy in the comprehensive energy system and the value of each energy quality evaluation index in the comprehensive energy system;

[0110] A relative proximity determination unit is used to determine the relative proximity of each energy supplier of each energy in the comprehensive energy system by using a superior and inferior solution distance method based on an energy grade weighted evaluation matrix corresponding to each energy in the comprehensive energy system;

[0111] The classification unit is used to classify the energy grades of each energy supplier of each energy in the comprehensive energy system according to the relative proximity corresponding to each energy supplier of each energy in the comprehensive energy system and the preset high and medium grade classification preset values ​​of each energy, so as to obtain the energy grade classification results.

[0112] The optimization module includes:

[0113] An acquisition unit, used to acquire energy consumption data of a user;

[0114] A cluster analysis unit, used to perform cluster analysis on the energy consumption data to obtain energy demand and typical energy consumption behavior of users;

[0115] A positioning unit, used for positioning the energy quality demand of the user according to the energy demand and typical energy consumption behavior;

[0116] An energy supply structure unit, used to obtain an energy supply structure by combining the energy grade classification result and the energy grade demand;

[0117] A generating unit is used to construct an energy network model according to the energy supply structure, and to generate an optimized operation strategy for the industrial integrated energy system based on the energy network model.

[0118] The optimized operation strategy of the industrial integrated energy system includes operation requirements and operation influencing factors. The operation requirements include economic operation requirements and clean operation requirements; the operation influencing factors include basic energy structure, general energy demand and energy cascade utilization.

[0119] The building blocks include:

[0120] A constraint condition determination unit, used to determine the constraint condition based on the operation influencing factors of the optimization operation strategy of the industrial integrated energy system;

[0121] An objective function determination unit, used to determine an objective function based on the operation requirements of the optimized operation strategy of the industrial integrated energy system and taking the maximization of economy and cleanliness as the optimization objectives;

[0122] A model building unit is used to build a multi-energy collaborative optimization model of an industrial comprehensive energy system taking into account the dual control of electricity and carbon according to the objective function and constraints.

[0123] The constraints include:

[0124] The linear constraint of gas turbine output by grade is expressed as: Among them, p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; p e,gt (t) is the electrical energy output of the gas turbine at time t; p max,e,gt and p min,e,gt are the upper and lower limits of electric energy output respectively; x gt (t) is the dispatch factor of the gas turbine at time t; a 1,gt and a 2,gt These are operating parameters fitted according to the actual conditions of the equipment; R gt is the ramp rate of the gas turbine; Δt is the unit time; p h,gt,mid (t) is the medium-grade thermal energy output of the gas turbine at time t; p h,gt,low (t) is the low-grade thermal energy output of the gas turbine at time t; η h,gt,mid is the energy efficiency coefficient of medium-grade thermal energy output of the gas turbine; η h,gt,low is the energy efficiency coefficient of the low-grade thermal energy output of the gas turbine;

[0125] Distributed photovoltaic operation constraints, expressed as: p e,pv (t) = η e,pv S pv G(t); where p e,pv (t) is the power output of distributed photovoltaic at time t; η e,pv is the energy conversion efficiency of distributed photovoltaics; S pvis the area of ​​the photovoltaic cell; G(t) is the solar radiation intensity per unit area at time t;

[0126] The battery operation constraints are expressed as: Among them, E es (t) is the battery capacity at time t; σ es is the self-discharge coefficient of the battery; E es,min It is the lower limit of the battery's capacity; E es,max is the upper limit of the battery's capacity; p es,ch (t) is the charging power of the battery at time t; p es,ch,min and p es,ch,max are the lower and upper limits of the battery charging power respectively; p es,dis (t) is the discharge power of the battery at time t; p es,dis,min and p es,dis,max are the lower and upper limits of the battery’s discharge power respectively; η es,ch is the charging efficiency of the battery; η es,dis is the discharge efficiency of the battery; x es,ch (t) and x es,dis (t) are the charging scheduling factor and discharging scheduling factor of the battery at time t; N es is the upper limit of the number of times the battery can be charged and discharged, and D represents the total operating time of the battery;

[0127] The operating constraints of the heat storage tank are expressed as: Among them, E hs (t) is the heat storage capacity of the heat storage tank at time t; σ hs is the self-release coefficient of the heat storage tank; E hs,min is the lower limit of the heat storage capacity of the heat storage tank; E hs,max is the upper limit of the heat storage capacity of the heat storage tank; p hs,ch (t) is the charging power of the heat storage tank at time t; p hs,ch,min and p hs,ch,max are the lower and upper limits of the charging power of the heat storage tank respectively; p hs,dis (t) is the energy release power of the heat storage tank; p hs,dis,min and p hs,dis,max are the lower and upper limits of the energy release power of the heat storage tank respectively; η hs,ch is the charging efficiency of the heat storage tank; η hs,dis is the energy release efficiency of the heat storage tank; x hs,ch (t) and x hs,dis (t) are the charging scheduling factor and discharging scheduling factor of the heat storage tank at time t; N hs It is the upper limit of the number of times the heat storage tank can be charged and discharged;

[0128] The energy conversion equipment operation constraints are expressed as: Among them, poutput (t) is the output power of the energy conversion device at time t; p input (t) is the input power of the energy conversion device at time t; η cov is the conversion efficiency of the energy conversion device; p output,max is the upper limit of output power; p output,min is the lower limit of output power;

[0129] The elastic electric load constraint is expressed as: Among them, L e,mov (t) is the elastic load of electric energy in the industrial park at time t; L e,mov,min (t) and L e,mov,max (t) are the upper and lower limits of the electric energy elastic load at time t respectively;

[0130] The electric vehicle charging load constraint is expressed as: Where I is the number of electric vehicles, J is the number of charging piles, and t i,start is the parking start time of the i-th electric car; t i,end is the parking end time of the i-th electric vehicle; SOC i (t i,start ) is the initial charge state of the ith electric vehicle when it is parked in the parking lot; E vc,i represents the battery capacity of the i-th car; p vc,charge Represents the electric energy provided by a single charging pile to an electric vehicle; x vc,i,j (t) represents the connection status between the i-th electric vehicle and the j-th charging pile; P vc,i is the charging demand of the i-th vehicle; L vc,charge (t) is the sum of the electric energy output of all charging piles at time t;

[0131] The power demand constraint of the data center server is expressed as: Among them, p dc,e (t) is the power load of the data center at time t; p I is the power consumption of the server in the data center when it is not loaded; μ dc (t) is the server utilization rate of the data center at time t; p P is the power consumption of the data center server under full load; m is the number of servers in the data center; μ dc,min To meet the minimum server utilization rate to complete the computing task; delay,max The maximum delay time for processing tasks;

[0132] The heat load constraint is expressed as: Among them, k wall is the heat dissipation coefficient of the building's exterior wall; S wall is the building's exterior wall area; k winis the heat dissipation coefficient of the building's exterior windows; S win is the area of ​​the building's exterior windows; G(t) is the solar radiation intensity per unit area at time t; S c is the building shading coefficient; p in (t) is the calorific value of indoor objects in the building at time t; L ACL,h (t) is the temperature control load generated by the building heating at time t; L ACL,c (t) is the temperature control load generated by the building cooling at time t; ρ is the air density; V air is the volume of air in the building; C air is the specific heat capacity of air; T out (t) is the ambient temperature at time t; T in (t) is the indoor set temperature at time t; T in,min and T in,max They are the lower and upper limits of the indoor set temperature respectively;

[0133] The high-grade and medium-grade energy utilization and recovery operation constraints are expressed as: p steam,low (t) = η steam L steam,mid (t), where L steam,mid (t) is the medium grade heat energy load at time t; p steam,low (t) is the low-grade heat energy recovered at time t; η steam is the heat recovery efficiency;

[0134] The energy supply and demand balance constraint of electric energy cascade utilization is expressed as: Among them, L e (t) is the production load with strong rigidity and high demand for electric energy quality at time t; p e,input (t) is the power output from the distribution network, gas turbines, and distributed photovoltaics at time t to meet the power input of other living loads, electric vehicle charging loads, and energy conversion loads; p eb (t) is the electric energy purchased from the power grid at time t; p es (t) is the redundant electricity sold by the industrial park to the grid at time t;

[0135] The energy supply and demand balance constraint of thermal energy cascade utilization is expressed as: Among them, L h (t) is the common low-grade heat energy load for production and life at time t; p h,output (t) is the thermal energy output of the energy conversion device at time t; p h,input (t) is the heat energy input to the energy conversion device at time t;

[0136] The supply and demand balance constraint of cold energy cascade utilization is expressed as: p c,output (t) = L c (t)+LACL,c (t), where p c,output (t) is the cooling energy output of the energy conversion device at time t; L c (t) is the low-grade cooling energy load for ordinary production and life at time t.

[0137] The objective function f is expressed as: in, The cost of purchasing energy for the comprehensive energy system of the industrial park, including the fuel cost of purchasing natural gas and the cost of purchasing electricity generated by electricity trading with the power grid; g (t) is the natural gas price at time t; p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; c eb (t) is the electricity price of the power grid at time t; c es (t) is the price at which users sell electricity to the grid at time t; p eb (t) is the user purchasing electricity from the grid; p es (t) is the electric energy sold by the user to the grid at time t; The operation and maintenance costs incurred during the application of energy production, storage and conversion equipment built and configured in the park's comprehensive energy system; z,om is the operation and maintenance cost per unit output of the zth device, p z (t) is the output of the zth device at time t, and Z is the total number of devices in the park's integrated energy system; is the cost of carbon tax; c ctax is the unit carbon tax; e gas is the unit carbon dioxide emission rate of natural gas; e grid is the unit carbon dioxide emission rate of electricity production; η grid It is the energy transmission efficiency of the power system.

[0138] The third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the first embodiment of the method for optimizing operation of dual-control of electricity and carbon for an integrated energy system when executing the computer program.

[0139] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the first embodiment of the method for optimizing operation of dual-control of electricity and carbon for an integrated energy system.

[0140] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the operation of electricity and carbon dual control for a comprehensive energy system, characterized in that: The following steps are involved: Carry out energy grade classification for each energy supplier of each energy source in the comprehensive energy system and obtain energy grade classification results; Determine the energy demand of the user, match the energy demand with the energy grade classification result, and obtain the optimal operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon; Based on the optimized operation strategy of the industrial integrated energy system, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed; Solve the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the comprehensive energy system; The various energy sources in the comprehensive energy system are scheduled using the optimal scheduling method for each energy source in the comprehensive energy system.

2. The electricity-carbon dual control optimization operation method for a comprehensive energy system according to claim 1 is characterized in that: The energy grade classification is performed on each energy supplier of each energy in the comprehensive energy system to obtain the energy grade classification result. Specifically include: Based on the weights of the energy quality evaluation indicators corresponding to the various energy sources in the comprehensive energy system and the values ​​of the energy quality evaluation indicators in the comprehensive energy system, respectively construct energy quality weighted evaluation matrices corresponding to the various energy sources in the comprehensive energy system; Based on the energy grade weighted evaluation matrix corresponding to each energy source in the integrated energy system, the superior and inferior solution distance method is used to determine the relative proximity of each energy supplier in the integrated energy system. According to the relative proximity of each energy supplier of each energy in the comprehensive energy system and the preset high and medium grade classification preset values ​​of each energy, the energy supplier of each energy is respectively classified into energy grades to obtain the energy grade classification results.

3. The electricity-carbon dual control optimization operation method for a comprehensive energy system according to claim 1 is characterized in that: The energy demand of the user is determined, and the energy demand is matched with the energy grade classification result to obtain an optimized operation strategy of the industrial comprehensive energy system considering the dual control of electricity and carbon, specifically including: Obtain users’ energy usage data; Performing cluster analysis on the energy consumption data to obtain energy demand and typical energy consumption behavior of users; Locate the energy quality demand of the user based on the energy demand and typical energy consumption behavior; Combining the energy grade classification result with the energy grade demand, an energy supply structure is obtained; An energy network model is constructed according to the energy supply structure, and an optimized operation strategy of the industrial integrated energy system is generated based on the energy network model.

4. The method for optimizing the operation of electricity-carbon dual control for a comprehensive energy system according to claim 3 is characterized in that: The optimized operation strategy of the industrial integrated energy system includes operation requirements and operation influencing factors. The operation requirements include economic operation requirements and clean operation requirements; the operation influencing factors include basic energy structure, general energy demand and energy cascade utilization.

5. The method for optimizing the operation of electricity-carbon dual control for a comprehensive energy system according to claim 4, characterized in that: The multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon is constructed based on the optimized operation strategy of the industrial comprehensive energy system, specifically including: Determining constraint conditions based on the operation influencing factors of the optimized operation strategy of the industrial integrated energy system; Based on the operation requirements of the optimized operation strategy of the industrial integrated energy system, an objective function is determined with the maximization of economy and cleanliness as the optimization goals; According to the objective function and constraints, a multi-energy collaborative optimization model of the industrial integrated energy system taking into account the dual control of electricity and carbon is constructed.

6. The electricity-carbon dual control optimization operation method for a comprehensive energy system according to claim 5 is characterized in that: The constraints include: The linear constraint of gas turbine output by grade is expressed as: Among them, p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; p e,gt (t) is the electrical energy output of the gas turbine at time t; p max,e,gt and p min,e,gt are the upper and lower limits of electric energy output respectively; x gt (t) is the dispatch factor of the gas turbine at time t; a 1,gt and a 2,gt These are operating parameters fitted according to the actual conditions of the equipment; R gt is the ramp rate of the gas turbine; Δt is the unit time; p h,gt,mid (t) is the medium-grade thermal energy output of the gas turbine at time t; p h,gt,low (t) is the low-grade thermal energy output of the gas turbine at time t; η h,gt,mid is the energy efficiency coefficient of medium-grade thermal energy output of the gas turbine; η h,gt,low is the energy efficiency coefficient of the low-grade thermal energy output of the gas turbine; Distributed photovoltaic operation constraints, expressed as: p e,pv (t) = η e,pv S pv G(t); where p e,pv (t) is the power output of distributed photovoltaic at time t; η e,pv is the energy conversion efficiency of distributed photovoltaics; S pv is the area of ​​the photovoltaic cell; G(t) is the solar radiation intensity per unit area at time t; The battery operation constraints are expressed as: Among them, E es (t) is the battery capacity at time t; σ es is the self-discharge coefficient of the battery; E es,min It is the lower limit of the battery's capacity; E es,max is the upper limit of the battery's capacity; p es,ch (t) is the charging power of the battery at time t; p es,ch,min and p es,ch,max are the lower and upper limits of the battery charging power respectively; p es,dis (t) is the discharge power of the battery at time t; p es,dis,min and p es,dis,max are the lower and upper limits of the battery’s discharge power respectively; η es,ch is the charging efficiency of the battery; η es,dis is the discharge efficiency of the battery; x es,ch (t) and x es,dis (t) are the charging scheduling factor and discharging scheduling factor of the battery at time t; N es is the upper limit of the number of times the battery can be charged and discharged, and D represents the total operating time of the battery; The operating constraints of the heat storage tank are expressed as: Among them, E hs (t) is the heat storage capacity of the heat storage tank at time t; σ hs is the self-release coefficient of the heat storage tank; E hs,min is the lower limit of the heat storage capacity of the heat storage tank; E hs,max is the upper limit of the heat storage capacity of the heat storage tank; p hs,ch (t) is the charging power of the heat storage tank at time t; p hs,ch,min and p hs,ch,max are the lower and upper limits of the charging power of the heat storage tank respectively; p hs,dis (t) is the energy release power of the heat storage tank; p hs,dis,min and p hs,dis,max are the lower and upper limits of the energy release power of the heat storage tank respectively; η hs,ch is the charging efficiency of the heat storage tank; η hs,dis is the energy release efficiency of the heat storage tank; x hs,ch (t) and x hs,dis (t) are the charging scheduling factor and discharging scheduling factor of the heat storage tank at time t; N hs It is the upper limit of the number of times the heat storage tank can be charged and discharged; The energy conversion equipment operation constraints are expressed as: Among them, p output (t) is the output power of the energy conversion device at time t; p input (t) is the input power of the energy conversion device at time t; η cov is the conversion efficiency of the energy conversion device; p output,max is the upper limit of output power; p output,min is the lower limit of output power; The elastic electric load constraint is expressed as: Among them, L e,mov (t) is the elastic load of electric energy in the industrial park at time t; L e,mov,min (t) and L e,mov,max (t) are the upper and lower limits of the electric energy elastic load at time t respectively; The electric vehicle charging load constraint is expressed as: Where I is the number of electric vehicles, J is the number of charging piles, and t i,start is the parking start time of the i-th electric car; t i,end is the parking end time of the i-th electric vehicle; SOC i (t i,start ) is the initial charge state of the ith electric vehicle when it is parked in the parking lot; E vc,i represents the battery capacity of the i-th car; p vc,charge Represents the electric energy provided by a single charging pile to an electric vehicle; x vc,i,j (t) represents the connection status between the i-th electric vehicle and the j-th charging pile; P vc ,i is the charging demand of the i-th vehicle; L vc,charge (t) is the sum of the electric energy output of all charging piles at time t; The power demand constraint of the data center server is expressed as: Among them, p dc,e (t) is the power load of the data center at time t; p I is the power consumption of the server in the data center when it is not loaded; μ dc (t) is the server utilization rate of the data center at time t; p P is the power consumption of the data center server under full load; m is the number of servers in the data center; μ dc,min To meet the minimum server utilization rate to complete the computing task; delay,max The maximum delay time for processing tasks; The heat load constraint is expressed as: Among them, k wall is the heat dissipation coefficient of the building's exterior wall; S wall is the building's exterior wall area; k win is the heat dissipation coefficient of the building's exterior windows; S win is the area of ​​the building's exterior windows; G(t) represents the solar radiation intensity per unit area at time t; S c is the building shading coefficient; p in (t) is the calorific value of indoor objects in the building at time t; L ACL,h (t) is the temperature control load generated by the building heating at time t; L ACL,c (t) is the temperature control load generated by the building cooling at time t; ρ is the air density; V air is the volume of air in the building; C air is the specific heat capacity of air; T out (t) is the ambient temperature at time t; T in (t) is the indoor set temperature at time t; T in,min and T in,max They are the lower and upper limits of the indoor set temperature respectively; The high-grade and medium-grade energy utilization and recovery operation constraints are expressed as: p steam,low (t) = η steam L steam,mid (t), where L steam,mid (t) is the medium grade heat energy load at time t; p steam,low (t) is the low-grade heat energy recovered at time t; η steam is the heat recovery efficiency; The energy supply and demand balance constraint of electric energy cascade utilization is expressed as: Among them, L e (t) is the production load with strong rigidity and high demand for electric energy quality at time t; p e,input (t) is the power output from the distribution network, gas turbines, and distributed photovoltaics at time t to meet the power input of other living loads, electric vehicle charging loads, and energy conversion loads; p eb (t) is the electric energy purchased from the power grid at time t; p es (t) is the redundant electricity sold by the industrial park to the grid at time t; The energy supply and demand balance constraint of thermal energy cascade utilization is expressed as: Among them, L h (t) is the common low-grade heat energy load for production and life at time t; p h,output (t) is the thermal energy output of the energy conversion device at time t; p h,input (t) is the heat energy input to the energy conversion device at time t; The supply and demand balance constraint of cold energy cascade utilization is expressed as: p c,output (t) = L c (t)+L ACL,c (t), where p c,output (t) is the cooling energy output of the energy conversion device at time t; L c (t) is the low-grade cooling energy load for ordinary production and life at time t.

7. The electricity-carbon dual control optimization operation method for a comprehensive energy system according to claim 6 is characterized in that: The objective function f is expressed as: in, The cost of purchasing energy for the comprehensive energy system of the industrial park, including the fuel cost of purchasing natural gas and the cost of purchasing electricity generated by electricity trading with the power grid; g (t) is the natural gas price at time t; p g,gt (t) is the natural gas energy consumption of the gas turbine at time t; c eb (t) is the electricity price of the power grid at time t; c es (t) is the price at which users sell electricity to the grid at time t; p eb (t) is the user purchasing electricity from the grid; p es (t) is the electric energy sold by the user to the grid at time t; The operation and maintenance costs incurred during the application of energy production, storage and conversion equipment built and configured in the park's comprehensive energy system; z,om is the operation and maintenance cost per unit output of the zth device, p z (t) is the output of the zth device at time t, and Z is the total number of devices in the park's integrated energy system; is the cost of carbon tax; c ctax is the unit carbon tax; e gas is the unit carbon dioxide emission rate of natural gas; e grid is the unit carbon dioxide emission rate of electricity production; η grid It is the energy transmission efficiency of the power system.

8. An electricity-carbon dual-control optimization operation device for a comprehensive energy system, characterized in that: include: A classification module is used to classify the energy grades of various energy suppliers in the comprehensive energy system and obtain energy grade classification results; An optimization module is used to determine the energy demand of users, match the energy demand with the energy grade classification result, and obtain an optimized operation strategy for the industrial comprehensive energy system considering the dual control of electricity and carbon; A construction module is used to construct a multi-energy collaborative optimization model of an industrial comprehensive energy system taking into account the dual control of electricity and carbon based on the optimized operation strategy of the industrial comprehensive energy system; A solution module is used to solve the multi-energy collaborative optimization model of the industrial comprehensive energy system taking into account the dual control of electricity and carbon, and obtain the optimal scheduling mode of each energy in the comprehensive energy system; The scheduling module is used to schedule various energy sources in the comprehensive energy system by adopting the optimization scheduling method of various energy sources in the comprehensive energy system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the steps of the electricity-carbon dual control optimization operation method for an integrated energy system as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing operation of dual-control of electricity and carbon for an integrated energy system as described in any one of claims 1 to 7 are implemented.

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