CN113780663A - Comprehensive energy system low-carbon scheduling method and system based on carbon transaction model - Google Patents
Comprehensive energy system low-carbon scheduling method and system based on carbon transaction model Download PDFInfo
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Abstract
The invention provides a comprehensive energy system low-carbon scheduling method and system based on a carbon trading model, and the method comprises the steps of firstly constructing a comprehensive energy system framework which integrates various renewable energy power generation and various energy forms of electricity, heat and gas; then, according to a price type electricity-heat comprehensive demand response model and a carbon transaction cost model of a carbon emission interval subsection, constructing a comprehensive energy system double-layer scheduling model with the aim of lowest energy cost for users at the upper layer and lowest purchased energy and carbon transaction cost at the lower layer; and finally, solving the model by using a CPLEX solver to obtain a global optimal solution. Based on the method, the invention also provides a comprehensive energy system low-carbon scheduling system based on the carbon transaction model, effectively solves the problems of environmental pollution and energy shortage in the prior art, improves the operation economy of the system, reduces the carbon emission of the system, promotes the consumption of renewable energy such as wind and light and the like, and has the advantages of scientific and reasonable method, strong applicability, good effect and the like.
Description
Technical Field
The invention belongs to the technical field of low-carbon economic operation of an integrated energy system, and particularly relates to a low-carbon scheduling method and system of the integrated energy system based on a carbon transaction model.
Background
The rapid development of economy has put a great pressure on environmental protection, and global warming caused by greenhouse gas emissions is an important issue facing the world. According to survey, carbon dioxide emission is a key factor of climate warming, electricity is a key industry of Chinese energy consumption, and the emission amount of carbon dioxide accounts for about 50% of the total national emission amount, so that the reduction of carbon dioxide emission becomes an important research hotspot of power system scheduling. Under the background, an Integrated Energy System (IES) integrates various energy forms such as electricity, heat, gas and the like, and is an energy production, supply and marketing integrated system which uniformly coordinates various links such as energy production, transmission and consumption. The construction of IES is an important technical means for improving the consumption of high-proportion renewable energy and controlling the emission of carbon dioxide, and has great practical significance for promoting energy transformation and ensuring low-carbon economic operation of the system. The carbon transaction mechanism can guide the transition of IES from high-emission energy consumption to low-emission energy consumption, is an important technical means for considering both the electricity economy and the environmental protection and low carbon property, and has obvious technical advantages in the application fields of large-scale new energy access, novel urban power grid construction and the like. In addition, the comprehensive demand response is used as an effective means for exciting the user to use the energy flexibility, and has very important significance for improving the system economy and saving energy and reducing emission.
In the prior art, most of carbon transactions adopted for scheduling problems of IES low-carbon economy are traditional carbon transactions with fixed transaction prices, and the fresh thermal load demand response is flexibly adjusted according to the thermal comfort demand change of users in different time periods.
Disclosure of Invention
In order to solve the technical problems, the invention provides a comprehensive energy system low-carbon scheduling method and system based on a carbon transaction model. The method has the advantages of being scientific and reasonable, capable of improving the running economy of the system, effectively reducing the carbon emission of the system, promoting the consumption capability of renewable energy sources such as wind and light, strong in applicability and good in effect, and adopts the following technical scheme:
the comprehensive energy system low-carbon scheduling method based on the carbon transaction model comprises the following steps of:
constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of the user;
inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system, and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user;
establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model and a comprehensive energy system lower layer scheduling model of carbon emission interval segmentation;
under the condition that the user energy plan meets a first constraint condition, taking the user energy plan as the load demand of a lower-layer scheduling model of the comprehensive energy system;
and performing deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition.
Further, the establishing of the price type electric heating comprehensive demand response model comprises the steps of fully utilizing the coupling complementary relation between electric energy and heat energy, and respectively establishing a mathematical model capable of time shifting electric load and a mathematical model capable of reducing heat load;
the mathematical model of the time-shiftable electric load is as follows: pL,t＝PFL,t+PSL,t(ii) a Then P isL,tThe total electric load of the user in the t period; pSL,tTime-shiftable electrical loads for a user during a time period t;
the mathematical model capable of reducing the heat load is as follows: hL,t＝HOL,t-HCL,t(ii) a Then HL,tReducing the heat load power for the user in the t period; hOL,tReducing the thermal load power before t time period for the user; hCL,tAnd reducing the thermal load power for the user in the t period.
Further, the method for establishing the upper scheduling model of the integrated energy system comprises the following steps:
constructing an optimization objective function of the comprehensive energy system by taking the minimum energy cost of a user as a target, wherein the expression of the optimization objective function is as follows:
wherein, F1Energy cost for the user; mu.stThe time-of-use electricity price is obtained; gamma raytIs the time-of-use heat value; kappa is a penalty factor characterizing the thermal comfort requirements of the user; kappa (H)CL,t)2Penalty cost for user reduction of thermal comfort caused by reduction of heating load;
determining a first constraint condition; the first constraint conditions comprise a time-shiftable electrical load constraint, a curtailable thermal load constraint and a thermal sense average predictor constraint; wherein the time-shiftable electrical load constraint is expressed by:
wherein
Is the minimum value of the time-shiftable load in the period t;
time-shiftable for t-period
A maximum value; alpha is the time-shifting load ratio;
the expression that can reduce the thermal load constraint is:
the expression of the thermal sense average prediction index constraint is as follows:
further, the method for obtaining the minimum heat supply amount of the user comprises the following steps:
the lowest indoor thermal comfort temperature acceptable by a user can be obtained according to the relation between the thermal sensing average prediction index and the indoor temperature;
obtaining the lowest heat supply load of the user according to the indoor lowest heat comfortable temperature acceptable by the user; wherein the expression of the heat supply amount is:
wherein,
room temperature at time t-1; t ist outOutdoor temperature for time period t; k is the comprehensive heat transfer coefficient of the building, and F is the surface area of the building; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air; Δ t is the time interval.
Further, the method for inputting the initial parameters of the upper scheduling model of the integrated energy system and solving the upper scheduling model of the integrated energy system to obtain the energy utilization plan of the user comprises the following steps:
inputting initial parameters of an upper-layer scheduling model of the comprehensive energy system, and solving the upper-layer scheduling model of the comprehensive energy system by adopting a CPLEX solver to obtain an energy utilization plan of a user; the initial parameters of the upper-layer scheduling model of the comprehensive energy system comprise an electric load predicted value, outdoor temperature, time-of-use electricity price, heat price parameters, building parameters, the number of scheduling time periods, upper limit values of optimized variables and lower limit values of the optimized variables.
Further, the method for establishing the stepped carbon trading cost model of the carbon emission interval segmentation comprises the following steps:
constructing an expression of the actual carbon emission of the system
Wherein, deltaGTIs the carbon emission intensity of the gas turbine; deltaGBCarbon emission intensity of a gas boiler; deltabeCarbon emission intensity for outsourced electrical energy; deltaP2GThe carbon capture intensity of the electric gas conversion equipment; pP2G,tElectrical power for the electrical gas conversion device during time t; beta is the conversion factor of the power supply of the gas turbine, PGT,tThe power supply amount of the gas turbine in the t period; hGT,tThe heat supply quantity of the gas turbine in the time period t; hGB,tProviding thermal power for the gas boiler during the time period t; pbe,tThe outsourcing electric energy power in the t period; t is a scheduling period;
total carbon emission quota expression for construction system
B is the total carbon emission quota of the system; lambda [ alpha ]GTIs a quota coefficient for the gas turbine; lambda [ alpha ]GBIs a quota coefficient of the gas boiler; lambda [ alpha ]beA quota coefficient for outsourced electrical energy;
constructing a stepped carbon transaction cost calculation model:
wherein, FcIs the carbon transaction cost; sigma is the base carbon trading price; omega is the carbon trading price increase range when the system is sold; epsilon is the carbon transaction price increase amplitude when the carbon emission right is purchased; p is the carbon emission interval step.
Further, the method for establishing the comprehensive energy system lower layer scheduling model comprises the following steps:
the method selects the minimum sum of the outsourcing electric energy of the comprehensive energy system, the natural gas energy cost and the carbon transaction cost as an optimization target, and the optimization target function is as follows:
min F2＝Fbe+Fbg+Fc
wherein, F2The total cost of the integrated energy system; fbeFor outsourcing electrical energy costs; fbgIs the cost of natural gas; thetabeThe price of outsourcing electric energy; thetabgIs the natural gas price;
purchasing a spinning reserve price from an upper-level power grid;
spinning reserve prices for purchases from the air grid; pbe,tPurchasing electric energy from an upper-level power grid for a period t; rbe,tRotating the standby power for a period of t; rGT,tBackup power provided to the gas turbine for time t;
providing gas-to-electricity efficiency for the gas turbine during the period t; etaP2GThe energy conversion efficiency of the electric gas conversion equipment; etaGBThe energy conversion efficiency of the gas boiler; qgIs natural gas with low heat value;
determining a second constraint condition; the second constraint conditions comprise energy balance constraint, gas turbine constraint, gas boiler constraint, electric-to-gas equipment constraint, electric energy storage constraint, rotary standby constraint and external network constraint;
the energy balance constraints include electrical power balance constraints and thermal power balance constraints,
wherein the energy balance constraint is expressed as:
PREG,ta predicted force value of the renewable energy source in a t period is obtained; pCH,tCharging power for the electric energy storage t time period; pDC,tDischarge power for the time period t of the electrical energy storage;
gas turbine constraints include unit output constraints and ramp constraints,
wherein the expression for the gas turbine constraint is:
is the upper limit of the gas turbine output;
is the lower limit of the gas turbine output;
the maximum downward ramp rate of the gas turbine;
the maximum upward ramp rate of the gas turbine; pi is the thermoelectric ratio of the gas turbine;
the gas boiler constraint comprises the output constraint and the climbing constraint of the gas boiler;
wherein the expression of the gas boiler constraint is:
the upper limit of the thermal output of the gas boiler;
the lower limit of the thermal output of the gas boiler;
the maximum downward slope climbing rate of the gas boiler;
the maximum upward slope rate of the gas boiler;
the expression for the electrical to gas equipment constraint is:
the maximum power of the electric gas conversion equipment;
electrical energy storage constraints including electrical energy storage output constraints and capacity constraints;
wherein the electrical energy storage constraint is expressed as
SmaxMaximum capacity to store energy for electricity; sminA minimum capacity to store energy for electricity; s(0)Is the electrical energy storage device cycle initial capacity; s(end)Capacity at the end of the electrical energy storage device;
is the maximum charging power; gamma rayCHMaximum charging efficiency;
is the maximum discharge power; gamma rayDCMaximum discharge efficiency;
rotating standby constraints, including gas turbine standby constraints, outsourcing electric energy standby constraints, standby constraints of electric energy storage and opportunity constraints of rotating standby;
wherein the expression of the spinning reserve constraint is:
Pt Wthe actual output value of the fan is t time period; pt PVThe actual output value of the photovoltaic is obtained in the t period; prProbability of being true for an event; psi is the confidence;
external network constraints including outsourcing power and outsourcing natural gas constraints;
wherein the expression of the external network constraint is:
the upper limit of the purchased electric energy;
the lower limit of the purchased electric energy;
upper limit for outsourcing natural gas;
is the lower limit of the purchased natural gas.
Further, the method for inputting the initial parameters of the comprehensive energy system lower layer scheduling model and solving the comprehensive energy system lower layer scheduling model comprises the following steps: inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, and solving the lower-layer scheduling model of the comprehensive energy system by adopting a CPLEX solver; the initial parameters of the comprehensive energy system lower layer scheduling model comprise: the method comprises the following steps of gas turbine parameters, gas boiler parameters, energy storage parameters, renewable energy source predicted values, scheduling time period numbers, upper limit values of optimized variables and lower limit values of the optimized variables.
Further, the output optimal scheduling scheme comprises a numerical value corresponding to the variable to be optimized and an optimal scheduling scheme of the comprehensive energy system for optimizing the objective function value.
The invention also provides a comprehensive energy system low-carbon scheduling system based on the carbon trading model, which comprises a first construction module, a first solving model, a second construction module, an input module and a second solving module;
the first construction module is used for constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of the user;
the first solving module is used for inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user;
the second construction module is used for establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model and a comprehensive energy system lower layer scheduling model of carbon emission interval segmentation;
the input module is used for taking the user energy plan as the load demand of a lower-layer scheduling model of the comprehensive energy system when the user energy plan meets a first constraint condition;
the second solving module is used for carrying out deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention provides a comprehensive energy system low-carbon scheduling method and system based on a carbon transaction model, which comprises the following steps of: the method comprises the steps of constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of the user; inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system, and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user; establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model of carbon emission interval segmentation and a comprehensive energy system lower layer scheduling model; under the condition that the user energy utilization plan meets the first constraint condition, taking the user energy utilization plan as the load requirement of a lower-layer scheduling model of the comprehensive energy system; and performing deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition. The comprehensive energy system low-carbon scheduling method based on the carbon transaction model also provides a comprehensive energy system low-carbon scheduling system based on the carbon transaction model. Firstly, constructing a comprehensive energy system framework which integrates various renewable energy power generation and various energy forms of electricity, heat and gas; then, according to a price type electricity-heat comprehensive demand response model and a carbon transaction cost model of a carbon emission interval subsection, constructing a comprehensive energy system double-layer scheduling model with the aim of lowest energy cost for users at the upper layer and lowest purchased energy and carbon transaction cost at the lower layer; and finally, a CPLEX solver is adopted to solve the model to obtain a global optimal solution, so that the problems of environmental pollution and energy shortage in the prior art are effectively solved, the running economy of the system is improved, the carbon emission of the system is reduced, the consumption of renewable energy such as wind and light is promoted, and the method has the advantages of being scientific and reasonable, strong in applicability, good in effect and the like.
Drawings
Fig. 1 is a flow chart of a low-carbon scheduling method for a comprehensive energy system based on a carbon transaction model according to embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of IES in embodiment 1 of the present invention;
FIG. 3 is a schematic diagram illustrating the time-sharing definition of the PMV index in embodiment 1 of the present invention;
FIG. 4 is a graph showing a comparison between the unit output and the total amount of purchased electric power output in example 1 of the present invention;
FIG. 5 is a diagram showing the variation of the total amount of thermal load reduction and the carbon emission according to the penalty factor in example 1 of the present invention;
FIG. 6 is a schematic diagram showing the variation of carbon emission and system cost according to the carbon transaction price in example 1 of the present invention;
fig. 7 is a schematic diagram of a comprehensive energy system low-carbon scheduling system based on a carbon transaction model according to embodiment 1 of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
Example 1
The comprehensive energy system low-carbon scheduling method based on the carbon transaction model, provided by the embodiment 1 of the invention, can better and effectively solve the problems of environmental pollution and energy shortage. Fig. 1 shows a flow chart of a low-carbon scheduling method of an integrated energy system based on a carbon transaction model in embodiment 1 of the present invention.
Starting to process the flow;
constructing an IES architecture with source-charge coordination, wherein the IES is an integrated energy system, and the constructed IES architecture comprises an electrical load, a thermal load, an electrical energy storage device and a P2G device, wherein the P2G device is a power to gas (P2G) device. Fig. 2 is a schematic structural diagram of IES in embodiment 1 of the present invention; the IES architecture comprises an electric load, a thermal load, an electric energy storage device and P2G equipment, wherein the electric load is borne by a fan, a photovoltaic device, a gas turbine and outsourcing electric energy, the thermal load is borne by the gas turbine and a gas boiler, the electric energy storage device improves the flexibility of system operation through storage and release of the electric energy, and the P2G equipment converts the electric energy into natural gas so as to provide natural gas energy for the gas turbine and the gas boiler.
Establishing a price type electricity-heat comprehensive demand response model, wherein the comprehensive demand response in the established electricity-heat comprehensive demand response model fully utilizes the coupling complementary relation between electric energy and heat energy, and mathematical models of time-shifting electric load and heat load reduction are respectively established, and the mathematical models specifically comprise:
establishing a mathematical model of a time-shiftable power load, wherein the power load of a user can independently adjust the power consumption and the time according to the price information of electricity to reduce the energy consumption cost of the user in addition to the fixed power load, and setting PL,tIs the total electric load of the user in the t period, the time-shifting electric load mathematical model of the user is shown as the formula (1),
PL,t＝PFL,t+PSL,t； (1)
then P isL,tThe total electric load of the user in the t period; pSL,tThe user is time-shiftable to power the load during the time period t.
A mathematical model capable of reducing heat load is established, the heat demand of a user has ambiguity, the user can reduce a certain amount of heat supply load within an acceptable range according to the requirement of the user on thermal comfort, and the reduction of the energy cost of the user is realizedLow and reduced carbon emission of the system, if HL,tFor the heat load power after the user cuts down in the t period, the mathematical model for cutting down the heat load is shown as the formula (2),
HL,t＝HOL,t-HCL,t； (2)
then HL,tReducing the heat load power for the user in the t period; hOL,tReducing the thermal load power before t time period for the user; hCL,tAnd reducing the thermal load power for the user in the t period.
Constructing an IES upper-layer scheduling model by taking the minimum energy cost of a user as a target, wherein the construction process of the IES upper-layer scheduling model comprises the following steps of constructing an expression of an optimized objective function, selecting the optimized target, setting the objective function of the IES upper-layer scheduling model to be the minimum energy cost of the user and setting F for fully mobilizing the active participation of the user in comprehensive demand response1For the energy cost of the user, the expression of the optimization objective function is shown in formula (3),
wherein, F1Energy cost for the user; mu.stThe time-of-use electricity price is obtained; gamma raytIs the time-of-use heat value; kappa is a penalty factor characterizing the thermal comfort requirements of the user; kappa (H)CL,t)2Penalty cost for user reduction of thermal comfort caused by reduction of heating load;
determining constraint conditions, wherein the constraint conditions of the IES upper layer scheduling model comprise time-shifting electric load constraint, reducible heat load constraint and PMV index constraint; wherein time-shiftable electrical load constraints are determined, and the time-shiftable electrical load constraints comprise a total fraction constraint and a power per time period constraint,
wherein
Is the minimum value of the time-shiftable load in the period t;
the maximum value of the time-shiftable load in the period t; alpha is the time-shifting load ratio;
determining reducible thermal load constraints
The minimum value of the heating load in the period t, the thermal load constraint can be reduced as shown in the formula (5),
determining PMV index constraint, time-sharing and limiting PMV value in a scheduling period because the user is in sleep state at night and has lower sensitivity to heat sensation than that in daytime, and then the PMV index constraint is shown as formula (6),
wherein PMV is mean thermal prediction.
The lowest heat supply demand of the user is obtained according to the PMV index, the obtaining of the lowest heat supply demand of the user comprises obtaining the lowest indoor heat comfortable temperature acceptable by the user and obtaining a heat load expression, particularly comprises obtaining the lowest indoor heat comfortable temperature acceptable by the user, the lowest indoor heat comfortable temperature acceptable by the user can be obtained according to the relation between the PMV index and the indoor temperature, the relation is shown as a formula (7),
wherein M is the energy metabolism rate of the human body; i isclThermal resistance of the garment; t issThe average temperature of human skin in a comfortable state; t ist inIndoor temperature for time period t;
the heat load expression is obtained, the transient heat balance equation of the building can describe the influence of the change of the heat supply quantity on the indoor temperature, the heat quantity and the temperature are linked, and then the required heat supply quantity can be obtained according to the indoor temperature, and H is setL,tFor the heat load power after the user is cut in the t period, the transient heat balance equation is transformed and solved to obtain a heat load expression as shown in the formula (8),
wherein,
room temperature at time t-1; t ist outOutdoor temperature for time period t; k is the comprehensive heat transfer coefficient of the building, and F is the surface area of the building; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air; Δ t is the time interval.
Inputting initial parameters of an IES upper-layer scheduling model, solving the IES upper-layer scheduling model by adopting a CPLEX solver, obtaining user energy planning data and generating an upper-layer solution, wherein the initial parameters input into the IES upper-layer scheduling model comprise an electric load predicted value, outdoor temperature, time-of-use electricity price and heat price parameters, building parameters, scheduling time period number and upper and lower limit values of each optimization variable.
Checking whether an upper-layer solution meets a first constraint condition, and if the first constraint condition is met, using the user energy plan as the load requirement of a lower-layer scheduling model of the comprehensive energy system; wherein the first constraint condition is:
the method comprises the following steps of constructing a stepped carbon transaction cost model of a carbon emission interval section, wherein the stepped carbon transaction refers to the step of dividing carbon emission into a plurality of intervals for carbon transaction, when the actual carbon emission is greater than a carbon emission quota, a carbon emission source needs to purchase a carbon emission limit from a carbon transaction market, the interval with more CO2 emission is higher in carbon transaction price, otherwise, when the actual carbon emission is less than the carbon emission quota, the carbon emission limit is sold to the carbon transaction market by the carbon emission source, the interval with smaller CO2 emission is higher in carbon transaction price, the stepped carbon transaction cost model of the carbon emission interval section comprises a system actual carbon emission expression, a system total carbon emission quota expression and a stepped carbon transaction cost calculation model, and the specific steps comprise:
constructing an actual carbon emission expression of the system, wherein three carbon emission sources of the system are respectively a gas turbine, a gas boiler and outsourced electric energy, meanwhile, the P2G equipment can absorb CO2 as a raw material in the process of converting electricity into gas, counteract CO2 emitted by a part of the carbon emission sources, and if D is the actual carbon emission of the system, the actual carbon emission expression of the system is as shown in a formula (9)
Wherein, deltaGTIs the carbon emission intensity of the gas turbine; deltaGBCarbon emission intensity of a gas boiler; deltabeCarbon emission intensity for outsourced electrical energy; deltaP2GThe carbon capture intensity of the electric gas conversion equipment; pP2G,tElectrical power for the electrical gas conversion device during time t; beta is the conversion factor of the power supply of the gas turbine, PGT,tThe power supply amount of the gas turbine in the t period; hGT,tThe heat supply quantity of the gas turbine in the time period t; hGB,tProviding thermal power for the gas boiler during the time period t; pbe,tThe outsourcing electric energy power in the t period; t is a scheduling period;
constructing a total carbon emission quota expression of the system, wherein the total carbon emission quota expression corresponds to a system carbon emission source, the total carbon emission quota of the system is formed by carbon emission quotas of a gas turbine, a gas boiler and outsourcing electric energy, if B is the total carbon emission quota of the system, the total carbon emission quota expression of the system is shown as a formula (10),
b is the total carbon emission quota of the system; lambda [ alpha ]GTIs a quota coefficient for the gas turbine; lambda [ alpha ]GBIs a quota coefficient of the gas boiler; lambda [ alpha ]beA quota coefficient for outsourced electrical energy;
establishing a stepped carbon transaction cost calculation model, wherein when B > D is negative and B < D is positive, the stepped carbon transaction cost calculation model is shown as a formula (11),
wherein, FcIs the carbon transaction cost; sigma is the base carbon trading price; omega is the carbon trading price increase range when the system is sold; epsilon is the carbon transaction price increase amplitude when the carbon emission right is purchased; p is the carbon emission interval step.
Constructing an IES lower-layer scheduling model with the aim of minimizing outsourcing energy and carbon transaction costs, wherein the construction process of the IES lower-layer scheduling model comprises the steps of selecting an optimization target and determining constraint conditions,
selecting an optimization target, selecting the minimum sum of IES outsourcing electric energy and natural gas energy cost and carbon transaction cost as the optimization target, considering the randomness of renewable energy power generation, and utilizing electric energy storage, a gas turbine and outsourcing electric energy to provide rotation reserve for the system, wherein the equipment F is2For the total cost of the IES, the expression for the optimization objective function is shown in equation (12),
wherein, F2The total cost of the integrated energy system; fbeFor outsourcing electrical energy costs; fbgIs the cost of natural gas; thetabeThe price of outsourcing electric energy; thetabgIs the natural gas price;
purchasing a spinning reserve price from an upper-level power grid;
spinning reserve prices for purchases from the air grid; pbe,tPurchasing electric energy from an upper-level power grid for a period t; rbe,tRotating the standby power for a period of t; rGT,tBackup power provided to the gas turbine for time t;
providing gas-to-electricity efficiency for the gas turbine during the period t; etaP2GThe energy conversion efficiency of the electric gas conversion equipment; etaGBThe energy conversion efficiency of the gas boiler; qgIs natural gas with low heat value;
determining a second constraint condition; the second constraint conditions comprise energy balance constraint, gas turbine constraint, gas boiler constraint, electric-to-gas equipment constraint, electric energy storage constraint, rotary standby constraint and external network constraint;
the energy balance constraints include electrical power balance constraints and thermal power balance constraints,
wherein the energy balance constraint is expressed as:
PREG,ta predicted force value of the renewable energy source in a t period is obtained; pCH,tCharging power for the electric energy storage t time period; pDC,tDischarge power for the time period t of the electrical energy storage;
gas turbine constraints include unit output constraints and ramp constraints,
wherein the expression for the gas turbine constraint is:
the gas boiler constraint comprises the output constraint and the climbing constraint of the gas boiler;
wherein the expression of the gas boiler constraint is:
the expression for the electrical to gas equipment constraint is:
electrical energy storage constraints including electrical energy storage output constraints and capacity constraints;
wherein the electrical energy storage constraint is expressed as
SmaxMaximum capacity to store energy for electricity; sminA minimum capacity to store energy for electricity; s(0)Is the electrical energy storage device cycle initial capacity; s(end)Capacity at the end of the electrical energy storage device;
is the maximum charging power; gamma rayCHMaximum charging efficiency;
is the maximum discharge power; gamma rayDCMaximum discharge efficiency;
rotating standby constraints, including gas turbine standby constraints, outsourcing electric energy standby constraints, standby constraints of electric energy storage and opportunity constraints of rotating standby;
wherein the expression of the spinning reserve constraint is:
Pt Wthe actual output value of the fan is t time period; pt PVThe actual output value of the photovoltaic is obtained in the t period; prProbability of being true for an event; psi is the confidence;
external network constraints including outsourcing power and outsourcing natural gas constraints;
wherein the expression of the external network constraint is:
And taking the user energy utilization plan obtained by solving the IES upper layer scheduling model as the load demand of the IES lower layer scheduling model, wherein the user energy utilization plan obtained by solving the IES upper layer scheduling model is the electric load and heat load demand of the user at each time period after the comprehensive demand response.
And performing deterministic transformation on the rotating standby constraint, wherein the rotating standby opportunity constraint is subjected to deterministic constraint transformation processing, and the processed model has a mixed integer linear programming structure.
Inputting initial parameters of an IES lower-layer scheduling model, and solving the lower-layer scheduling model by using a CPLEX solver to obtain a lower-layer solution, wherein the input initial parameters comprise parameters of a gas turbine, parameters of a gas boiler, energy storage parameters, a renewable energy source predicted value, the number of scheduling time segments and upper and lower limit values of each optimized variable.
And checking whether the lower-layer solution meets a second constraint condition, and outputting the optimal scheduling scheme if the second constraint condition is met, wherein the second constraint condition comprises equations (13) - (19).
And outputting an optimal scheduling scheme which comprises a numerical value corresponding to the variable to be optimized and an optimal scheduling scheme of the comprehensive energy system for optimizing the objective function value.
The use effect of the comprehensive energy system low-carbon scheduling method based on the carbon transaction model is described below, in this embodiment, based on the actual electrical load of a certain IES, the outdoor temperature and the wind-solar output, four operation modes are set for example comparison analysis to verify the effectiveness of the model constructed by the method, and the set four modes are:
mode 1: traditional carbon trading, without consideration of comprehensive demand response;
mode 2: traditional carbon trading, considering comprehensive demand response;
mode 3: step-wise carbon trading, without considering comprehensive demand response;
mode 4: step-wise carbon trading, considering comprehensive demand response;
in the above mode, the optimized scheduling result applying the method of the present invention is as follows: fig. 4 is a graph comparing the total output of each device and the outsourcing electric energy in different modes, fig. 5 is a graph illustrating the variation of thermal load reduction and system carbon emission under different penalty factors in the mode 4, and fig. 6 is a graph illustrating the variation of the cost of energy purchase, the total cost and the carbon emission in the mode 4 with the increase of the carbon transaction price.
As can be seen from fig. 4, the outsourcing electric energy and the gas boiler output are reduced and the gas turbine output is increased after considering the stepped carbon transaction, because the outsourcing electric energy is reduced and the gas turbine output is increased to suppress the high carbon emission of the outsourcing electric energy after considering the stepped carbon transaction, and the gas turbine output is reduced to maintain the thermal load balance because the thermal output of the thermoelectric restraint gas turbine is increased. The explanation considers that the stepped carbon transaction can effectively restrict the high-carbon unit, improve the output of the low-carbon unit and further reduce the overall carbon emission level of the system.
As can be seen from fig. 5, as the penalty factor is gradually increased, the heat load reduction amount is gradually decreased, but the carbon emission amount is gradually increased. The penalty factor reflects the requirement of the user on the thermal comfort degree, the larger the value is, the higher the requirement of the user on the thermal comfort degree is represented, so the penalty factor is reasonably set, and the carbon emission of the system is reduced while the requirement on the thermal comfort degree of the user is met.
As can be seen from fig. 6, the carbon emission amount decreases as the carbon transaction price increases, and the carbon transaction cost, i.e., the difference between the total cost and the energy purchase cost, increases continuously because the stepped carbon transaction reduces the outsourcing electric energy, increases the power output of the gas turbine, and thus reduces the carbon emission amount. When the carbon trading price is 1000 yuan/t, the carbon emission and the energy purchasing cost of the system are kept unchanged because the gas turbine reaches the output upper limit, the unit output is not changed any more, the capacity of the gas turbine is smaller than that of outsourcing electric energy, and the actual carbon emission of the system is not smaller than the quota, so the total cost is increased in proportion with the increase of the carbon trading price.
Table 1 shows the carbon emissions, carbon transaction costs, energy purchase costs and total cost for the system in different modes.
| Mode(s) | Carbon emission (t) | Cost of carbon transaction (Yuan) | Cost of purchasing energy (Yuan) | Total cost (Yuan) |
| |
14.26 | 89.57 | 11147.33 | 11236.89 |
| Mode 2 | 14.13 | 102.74 | 10768.60 | 10871.34 |
| Mode 3 | 12.80 | 121.88 | 11328.60 | 11450.47 |
| Mode 4 | 12.43 | 144.38 | 10979.82 | 11124.20 |
As can be seen from table 1, the gradual decrease in mode 1 to mode 4 carbon emissions illustrates carbon emissions for the beneficial suppression system in view of the stepped carbon trading and the integrated demand response; wherein mode 4 considers the integrated demand response compared to mode 3, although the carbon transaction cost is increased, the energy purchase cost is reduced, and the total cost of the system is reduced; compared with the mode 2, the mode 4 has the advantages that after the step-type carbon transaction is considered, the carbon emission is reduced by 12.03%, the total cost is increased by only 2.33%, and the mode provided by the invention can effectively reduce the carbon emission and also considers the operation economy of the system.
In summary, the comprehensive energy system low-carbon scheduling method based on the carbon transaction model of the invention includes firstly constructing an IES framework which integrates various renewable energy power generation and various energy forms of electricity, heat and gas; then according to a price type electricity-heat comprehensive demand response model and a carbon transaction cost model of a carbon emission interval subsection, an IES double-layer scheduling model with the upper layer aiming at the lowest energy cost of users and the lowest outsourcing energy and carbon transaction cost of the lower layer is constructed; and finally, a CPLEX solver is adopted to solve the model to obtain a global optimal solution, so that the problems of environmental pollution and energy shortage in the prior art are effectively solved, the running economy of the system is improved, the carbon emission of the system is reduced, the consumption of renewable energy such as wind and light is promoted, and the method has the advantages of being scientific and reasonable, strong in applicability, good in effect and the like.
Example 2
Based on the carbon transaction model-based low-carbon scheduling method for the integrated energy system in embodiment 1 of the present invention, embodiment 2 of the present invention further provides a carbon transaction model-based low-carbon scheduling system for the integrated energy system, and fig. 7 is a schematic diagram of the carbon transaction model-based low-carbon scheduling system for the integrated energy system in embodiment 1 of the present invention, where the system includes a first building module, a first solving model, a second building module, an input module, and a second solving module;
the first construction module is used for constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of users;
the first solving module is used for inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user;
the second construction module is used for establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model and a comprehensive energy system lower layer scheduling model of carbon emission interval segmentation;
the input module is used for taking the user energy plan as the load demand of a lower-layer scheduling model of the comprehensive energy system when the user energy plan meets a first constraint condition;
the second solving module is used for carrying out deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include elements inherent in the list. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. In addition, parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of corresponding technical solutions in the prior art, are not described in detail so as to avoid redundant description.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the scope of the present invention is not limited thereto. Various modifications and alterations will occur to those skilled in the art based on the foregoing description. And are neither required nor exhaustive of all embodiments. On the basis of the technical scheme of the invention, various modifications or changes which can be made by a person skilled in the art without creative efforts are still within the protection scope of the invention.
Claims (10)
1. The comprehensive energy system low-carbon scheduling method based on the carbon transaction model is characterized by comprising the following steps of:
constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of the user;
inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system, and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user;
establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model and a comprehensive energy system lower layer scheduling model of carbon emission interval segmentation;
under the condition that the user energy plan meets a first constraint condition, taking the user energy plan as the load demand of a lower-layer scheduling model of the comprehensive energy system;
and performing deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition.
2. The integrated energy system low-carbon scheduling method based on the carbon transaction model is characterized in that the establishing of the price type electric heating integrated demand response model comprises the steps of fully utilizing the coupling complementary relation between electric energy and heat energy, and respectively establishing a mathematical model capable of time shifting electric loads and a mathematical model capable of reducing heat loads;
the mathematical model of the time-shiftable electric load is as follows: pL,t＝PFL,t+PSL,t(ii) a Then P isL,tThe total electric load of the user in the t period; pSL,tTime-shiftable electrical loads for a user during a time period t;
the mathematical model capable of reducing the heat load is as follows: hL,t＝HOL,t-HCL,t(ii) a Then HL,tReducing the heat load power for the user in the t period; hOL,tReducing the thermal load power before t time period for the user; hCL,tAnd reducing the thermal load power for the user in the t period.
3. The carbon transaction model-based low-carbon scheduling method for the integrated energy system, according to claim 2, wherein the method for establishing the upper scheduling model of the integrated energy system comprises the following steps:
constructing an optimization objective function of the comprehensive energy system by taking the minimum energy cost of a user as a target, wherein the expression of the optimization objective function is as follows:
wherein, F1Energy cost for the user; mu.stThe time-of-use electricity price is obtained; gamma raytIs the time-of-use heat value; kappa is a penalty factor characterizing the thermal comfort requirements of the user; kappa (H)CL,t)2Penalty cost for user reduction of thermal comfort caused by reduction of heating load;
determining a first constraint condition; the first constraint conditions comprise a time-shiftable electrical load constraint, a curtailable thermal load constraint and a thermal sense average predictor constraint; wherein the time-shiftable electrical load constraint is expressed by:
wherein
Is the minimum value of the time-shiftable load in the period t;
the maximum value of the time-shiftable load in the period t; alpha is the time-shifting load ratio;
the expression that can reduce the thermal load constraint is:
the expression of the thermal sense average prediction index constraint is as follows:
4. the integrated energy system low-carbon scheduling method based on the carbon transaction model is characterized in that the method for obtaining the minimum heat supply of the user comprises the following steps:
the lowest indoor thermal comfort temperature acceptable by a user can be obtained according to the relation between the thermal sensing average prediction index and the indoor temperature;
obtaining the lowest heat supply load of the user according to the indoor lowest heat comfortable temperature acceptable by the user; wherein the expression of the heat supply amount is:
wherein,
room temperature at time t-1; t ist outOutdoor temperature for time period t; k is the comprehensive heat transfer coefficient of the building, and F is the surface area of the building; v is the building volume; cairIs the specific heat capacity of the indoor air; rhoairIs the density of the indoor air; Δ t is the time interval.
5. The integrated energy system low-carbon scheduling method based on the carbon trading model of claim 4, wherein the method for inputting the initial parameters of the integrated energy system upper scheduling model and solving the integrated energy system upper scheduling model to obtain the energy utilization plan of the user comprises the following steps:
inputting initial parameters of an upper-layer scheduling model of the comprehensive energy system, and solving the upper-layer scheduling model of the comprehensive energy system by adopting a CPLEX solver to obtain an energy utilization plan of a user; the initial parameters of the upper-layer scheduling model of the comprehensive energy system comprise an electric load predicted value, outdoor temperature, time-of-use electricity price, heat price parameters, building parameters, the number of scheduling time periods, upper limit values of optimized variables and lower limit values of the optimized variables.
6. The carbon transaction model-based integrated energy system low-carbon scheduling method of claim 5, wherein the method for establishing the carbon emission interval segmented stepwise carbon transaction cost model comprises the following steps:
constructing an expression of the actual carbon emission of the system
Wherein, deltaGTIs the carbon emission intensity of the gas turbine; deltaGBCarbon emission intensity of a gas boiler; deltabeCarbon emission intensity for outsourced electrical energy; deltaP2GThe carbon capture intensity of the electric gas conversion equipment; pP2G,tElectrical power for the electrical gas conversion device during time t; beta is the conversion factor of the power supply of the gas turbine, PGT,tThe power supply amount of the gas turbine in the t period; hGT,tThe heat supply quantity of the gas turbine in the time period t; hGB,tProviding thermal power for the gas boiler during the time period t; pbe,tThe outsourcing electric energy power in the t period; t is a scheduling period;
total carbon emission quota expression for construction system
B is the total carbon emission quota of the system; lambda [ alpha ]GTIs a quota coefficient for the gas turbine; lambda [ alpha ]GBIs a quota coefficient of the gas boiler; lambda [ alpha ]beA quota coefficient for outsourced electrical energy;
constructing a stepped carbon transaction cost calculation model:
wherein, FcIs the carbon transaction cost; sigma is the base carbon trading price; omega is the carbon trading price increase range when the system is sold; epsilon is the carbon transaction price increase amplitude when the carbon emission right is purchased; p is the carbon emission interval step.
7. The carbon transaction model-based low-carbon scheduling method for the integrated energy system, according to claim 6, wherein the method for establishing the lower-layer scheduling model of the integrated energy system comprises the following steps:
the method selects the minimum sum of the outsourcing electric energy of the comprehensive energy system, the natural gas energy cost and the carbon transaction cost as an optimization target, and the optimization target function is as follows:
wherein, F2The total cost of the integrated energy system; fbeFor outsourcing electrical energy costs; fbgIs the cost of natural gas; thetabeThe price of outsourcing electric energy; thetabgIs the natural gas price;
purchasing a spinning reserve price from an upper-level power grid;
spinning reserve prices for purchases from the air grid; pbe,tPurchasing electric energy from an upper-level power grid for a period t; rbe,tRotating the standby power for a period of t; rGT,tBackup power provided to the gas turbine for time t;
providing gas-to-electricity efficiency for the gas turbine during the period t; etaP2GThe energy conversion efficiency of the electric gas conversion equipment; etaGBThe energy conversion efficiency of the gas boiler; qgIs natural gas with low heat value;
determining a second constraint condition; the second constraint conditions comprise energy balance constraint, gas turbine constraint, gas boiler constraint, electric-to-gas equipment constraint, electric energy storage constraint, rotary standby constraint and external network constraint;
the energy balance constraints include electrical power balance constraints and thermal power balance constraints,
wherein the energy balance constraint is expressed as:
PREG,ta predicted force value of the renewable energy source in a t period is obtained; pCH,tCharging power for the electric energy storage t time period; pDC,tDischarge power for the time period t of the electrical energy storage;
gas turbine constraints include unit output constraints and ramp constraints,
wherein the expression for the gas turbine constraint is:
is the upper limit of the gas turbine output;
is the lower limit of the gas turbine output;
the maximum downward ramp rate of the gas turbine;
the maximum upward ramp rate of the gas turbine; pi is the thermoelectric ratio of the gas turbine;
the gas boiler constraint comprises the output constraint and the climbing constraint of the gas boiler;
wherein the expression of the gas boiler constraint is:
the upper limit of the thermal output of the gas boiler;
the lower limit of the thermal output of the gas boiler;
the maximum downward slope climbing rate of the gas boiler;
the maximum upward slope rate of the gas boiler;
the expression for the electrical to gas equipment constraint is:
the maximum power of the electric gas conversion equipment;
electrical energy storage constraints including electrical energy storage output constraints and capacity constraints;
wherein the electrical energy storage constraint is expressed as
SmaxMaximum capacity to store energy for electricity; sminA minimum capacity to store energy for electricity; s(0)Is the electrical energy storage device cycle initial capacity; s(end)Capacity at the end of the electrical energy storage device;
is the maximum charging power; gamma rayCHMaximum charging efficiency;
is the maximum discharge power; gamma rayDCMaximum discharge efficiency;
rotating standby constraints, including gas turbine standby constraints, outsourcing electric energy standby constraints, standby constraints of electric energy storage and opportunity constraints of rotating standby;
wherein the expression of the spinning reserve constraint is:
Pt Wthe actual output value of the fan is t time period; pt PVThe actual output value of the photovoltaic is obtained in the t period; prProbability of being true for an event; psi is the confidence;
external network constraints including outsourcing power and outsourcing natural gas constraints;
wherein the expression of the external network constraint is:
the upper limit of the purchased electric energy;
the lower limit of the purchased electric energy;
upper limit for outsourcing natural gas;
is the lower limit of the purchased natural gas.
8. The integrated energy system low-carbon scheduling method based on the carbon transaction model according to claim 7, wherein initial parameters of the integrated energy system lower layer scheduling model are input, and the method for solving the integrated energy system lower layer scheduling model comprises the following steps: inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, and solving the lower-layer scheduling model of the comprehensive energy system by adopting a CPLEX solver; the initial parameters of the comprehensive energy system lower layer scheduling model comprise: the method comprises the following steps of gas turbine parameters, gas boiler parameters, energy storage parameters, renewable energy source predicted values, scheduling time period numbers, upper limit values of optimized variables and lower limit values of the optimized variables.
9. The carbon transaction model-based low-carbon scheduling method for the integrated energy system according to claim 8, wherein the output optimal scheduling scheme comprises a value corresponding to a variable to be optimized and an optimal scheduling scheme for the integrated energy system that optimizes a function value.
10. The comprehensive energy system low-carbon scheduling system based on the carbon trading model is characterized by comprising a first construction module, a first solving model, a second construction module, an input module and a second solving module;
the first construction module is used for constructing a source-load coordinated comprehensive energy system; the comprehensive energy system comprises an electric load, a thermal load, an electric energy storage device and an electric gas conversion device; establishing an upper layer model of the comprehensive energy system based on the comprehensive energy system, wherein the upper layer model comprises a price type electric heating comprehensive demand response model, a comprehensive energy system upper layer scheduling model and the minimum required heat supply of the user;
the first solving module is used for inputting initial parameters of the upper-layer scheduling model of the comprehensive energy system and solving the upper-layer scheduling model of the comprehensive energy system to obtain an energy utilization plan of a user;
the second construction module is used for establishing a lower layer model of the comprehensive energy system; the lower layer model comprises a stepped carbon transaction cost model and a comprehensive energy system lower layer scheduling model of carbon emission interval segmentation;
the input module is used for taking the user energy plan as the load demand of a lower-layer scheduling model of the comprehensive energy system when the user energy plan meets a first constraint condition;
the second solving module is used for carrying out deterministic conversion on the rotating standby constraint, inputting initial parameters of a lower-layer scheduling model of the comprehensive energy system, solving the lower-layer scheduling model of the comprehensive energy system to obtain the optimal cost of the comprehensive energy system, and outputting an optimal scheduling scheme when the optimal cost meets a second constraint condition.
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